00003553 file
00003700 network
00003846 i/o
00004018 instrument
00004194 run
00004333 file@new
00004675 file@open
00005205 file@close
00005451 file@save
00005731 file@saveas
00007506 file@reverttosaved
00007675 file@printnetwork
00009095 file@savewindow
00009236 file@help
00023825 file@currentdirectory
00024018 file@oscall
00024185 file@quit
00024291 network@backpropbuilder
00036838 network@instanet
00056122 network@parameters
00057715 network@displaystyle
00059162 network@l/rschedule
00060317 network@loadcontrolstrategy
00061909 network@loaderrorfunctiontable
00062663 network@initialize
00063732 network@purge
00064002 network@randomizeweights
00065681 network@jogweights
00066564 network@zerodeltaweights
00067170 network@randomnumberseed
00067792 network@hideconnections
00067999 network@showconnections
00068336 i/o@parameters
00071494 i/o@minmaxtable
00072792 i/o@rewindinput
00073331 i/o@attentionio
00073624 instrument@initialize
00073824 instrument@selectprobe
00075355 instrument@outline
00075721 instrument@disable
00075962 instrument@enable
00076212 instrument@revert
00076392 run@learn
00077691 run@test
00078666 run@recall
00080032 run@recallstart
00080331 run@recallstep
00080969 run@flashcode
00082253 run@explain
00083779 run@explainnow
00084258 run@changecounters
00085293 run@checkpoints
00088551 arrow
00090247 layer@add
00094825 layer@edit
00097872 layer@clone
00098243 layer@delete
00098481 layer@probe
00100790 layer@initialize
00101708 layer@randomizeweights
00102523 layer@jogweights
00103112 layer@zerodeltaweights
00103313 layer@showconnections
00103910 layer@hideconnections
00104425 layer@l/rschedule
00107855 pe@add
00108507 pe@edit
00111163 pe@clone
00112572 pe@delete
00112950 pe@probe
00115233 pe@initialize
00115719 pe@randomizeweights
00116018 pe@jogweights
00116272 pe@zerodeltaweights
00116461 pe@showconnections
00117061 pe@hideconnections
00117579 graph@easyprobe
00121388 graph@add
00129984 graph@edit
00138194 graph@clone
00146719 graph@delete
00147006 graph@instaprobe
00151126 graph@activeconnections
00153429 graph@initialize
00153720 graph@hide
00154056 graph@show
00154252 layertolayer@add
00158725 layertolayer@edit
00162642 layertolayer@delete
00162938 layertolayer@probe
00165268 layertolayer@initialize
00165745 layertolayer@randomizeweights
00166142 layertolayer@jogweights
00166483 layertolayer@zerodeltaweights
00166816 layertolayer@showconnections
00167360 layertolayer@hideconnections
00167779 petope@add
00171856 petope@edit
00175630 petope@clone
00176190 petope@delete
00176581 petope@probe
00179296 petope@initialize
00179744 petope@randomizeweights
00180143 petope@jogweights
00180605 petope@zerodeltaweights
00180875 petope@hideconnections
00181174 petope@showconnections
00181352 fanin@add
00185966 fanin@edit
00189885 fanin@delete
00190186 fanin@probe
00192504 fanin@initialize
00192953 fanin@randomizeweights
00193335 fanin@jogweights
00193650 fanin@zerodeltaweights
00193966 fanin@showconnections
00194495 fanin@hideconnections
00194901 fanout@add
00199491 fanout@edit
00203406 fanout@delete
00203707 fanout@probe
00206026 fanout@initialize
00206498 fanout@randomizeweights
00206882 fanout@jogweights
00207208 fanout@zerodeltaweights
00207521 fanout@showconnections
00208048 fanout@hideconnections
00208458 fanout@clone
:file
             --- Help about File Menu ---

Gives you control over the major file operations.  Refer
to individual items for details.
:network
           --- Help about Network Menu ---

Gives you control over the network level operations. Refer
to individual items for details.
:i/o
             --- Help about I/O Menu ---

Gives you control over the Input and Output of data to/from
your network. Refer to individual items for details.
:instrument
           --- Help about Instruments Menu ---

Allows you to do global operations on the instruments
attached to a Network.  Refer to individual items for
details.
:run
             --- Help about Run Menu ---

Allows you to train or test your network.  Refer to
individual items for details.
:file@new
             --- Help about File/New ---

Removes whatever is currently on the screen and provides a
blank work window.  If you have a network currently loaded,
you will be warned before it is deleted.  Select New if you
wish to hand-craft your network layer by layer, rather than
use BackProp Builder, InstaNet or Open.

:file@open
             --- Help about File/Open ---

This command allows you load a network from disk.  If the
currently loaded network has been changed since the last time
it was saved, you will be asked to confirm deleting it.

To select a network for loading, either click on it and then
click Open, or simply double click on the network within the
scroll box.

If the network you want to load is not in the current
directory, you must first change directories using the
File/Change Directory menu selection.

:file@close
             --- Help about File/Close---
Removes the current network from the screen and from memory. If
the currently loaded network has been changed since the last
time it was saved, you will be asked to confirm deleting it.

:file@save
             --- Help about File Save---

If you have previously saved your network to a disk file, this
command will overwrite that file with your latest changes. If
you have not previously saved it, this command is the same as
the File/Save As... command.

:file@saveas
            --- Help about File/Save As---
            
Allows you to save your currently loaded network to a disk file.
A dialog box is provided which allows to specify a name for the
file, and also to specify the mode of saving: binary, ASCII, or
annotated ASCII.  Network files have a ".nnd" extension; you
should not include this extension when you are specifying a name
for the file.

Binary mode is the fastest, and for large networks, produces the
smallest file.  This mode uses the native binary representation
for the connections and so may not be compatible with versions
running on other computer platforms.

ASCII Mode saves the network entirely in text form.  Elements of
the data structures are saved one item per line.  Files saved in
ASCII mode can be transported to other computer platforms without
modification.  Connection weights are saved to 9 decimal places.

Annotated ASCII mode is similar to ASCII mode, but each line is
annotated with a comment describing the meaning of the particular
data item.  As with ASCII mode, Annotated ASCII files can be
transported between different computer platforms.  Because of the
comments added, networks saved in Annotated ASCII mode will be
significantly larger than those saved in the other two modes.  It
is recommended that you only use this mode for very small network
if you are interested in examining the network file.  Note also
that while NeuralWare is committed to providing upward
compatibilty for network files,  the internal format of these
files may change from one release to the next.

Network files that will be used for Designer pack should be saved
in ASCII or Annotated ASCII using the version of NeuralWorks
shipped with Designer Pack.

:file@reverttosaved
           --- Help about File/Revert to Saved ---

Abandons all changes and reloads the last saved version of the
currently loaded network.


:file@printnetwork
              --- Help about File/Print Network ---

Send a listing of the network to the display, a file or the printer. 
The print out should be reasonably self-explanatory.  It will 
include information from the following commands and tools:

  Parameters command from the Network menu

  Parameters command from the IO menu

  L/R Schedule command from the Network menu

  Edit tool from the Layer object palette

  Edit tool from the PE object palette

Learning and Recall Schedules may be duplicated more than once if 
more than one layer references them.

By checking the connections box, you can also choose to have the 
connections printed.

Processing element connections are displayed in the same format 
as in the PE/Edit tool dialog box.  The columns are as follows:

PE          The name (or number) of the processing element where 
            the connection originated.

Input       The current output of the source PE (the input to 
            this connection).

Weight      The value of the connecting weight between the 
            source PE and this one.

Weight Type The mode of weight (V=Variable, F=Fixed, S=Set, 
            M=Modified, D=Disabled) and the connection scope 
            (a=Absolute, r=Relative, l=Local).

Delta Weight
            The last weight change or accumulated weight change 
            (Cum Delta Rule).


:file@savewindow
              --- Help about File/Print Network ---

Creates a TIF (Tagged Image Format) file of the current window
display.
:file@help
                          --- HELP ---

Many of the help files contain more information than will fit on your 
screen.  Use the <PgDn> key, <SPACE> bar  or the left mouse button
to page down through the information.  Use the right mouse button or
the <Esc> key to return to NWorks.

This is the main "help", and has information about:

  * Loading a network
  * Short cuts for editing objects within a network
  * Short cuts for moving (or resizing) objects within a network  
  * What's in the NWorks menus
  * Where to set options

For help on menu items, place the cursor over the menu item and
press the F1 key.

For help in dialog boxes, place the cursor over the help button
that appears in the dialog, and click on the left mouse button.

For help about the Object Palette (at the left of the screen), 
place the cursor over the "current tool" icon at the top of the
tool palette, and click on the left mouse button.

For help about the Object Palette (at the left of the screen), 
click on the current tool display box at the top of the palette.



                        Loading a Network


  1.Select Open from the File menu.

  2.Click on the file you want to load, then click OK.  (You can 
    also simply double click on the file name.)


Example: Double click on the network called "sample" from the list of
network file names in the scroll window.  You will see a two-input 
network designed to solve the "Exclusive OR" problem.  Refer to the 
tutorial manual "Using NWorks" Tutorial for more information.



                  Editing Objects in a Network


Select and Edit a Processing Element (PE)


  1.Double click on a PE.

The PE information is loaded into the PE/Edit tool dialog box. 
[See the "Processing Element (PE) Palette" chapter in the Reference 
guide.]

  2. Double click on a weight in a Hinton diagram.

This pops up the PE/Edit tool for the destination Processing
Element; the selected weight is highlighted.



Edit IO Parameters for network

  1.Double click away from any PEs.

This short cut has the same effect as selecting the Parameters
command from the IO/Menu.  (See the "IO Menu" chapter in the 
Reference Guide.)



             Moving/Resizing Objects in the Network


Moving a Layer 

  1.Click and hold the mouse button over any PE in that layer.

  2.Drag marquee into position.

  3.Release mouse button.

  CAUTION: The order in which data gets processed through the
  network is critically dependent on the vertical ordering of
  the layers on the screen.  Do not change this ordering
  unless you have good reason to.


Moving a Network

  1.Click and hold the mouse button away from any PEs.

  2.Drag marquee into position.

  3.Release mouse button.


Moving an Instrument

  1.Click and hold the mouse button over the instrument graph.

  2.Drag the instrument outline box into position.

  3.Release the mouse button.


Resizing an Instrument


  1. Click and hold just within any corner of the instrument 
     graph (mouse pointer becomes "corner").

  2.Drag the outline box corner to shrink or expand the graph.



                   What's in the NWorks Menus


File Menu (see the File Menu chapter in the Reference Guide)


New         Clears the NWorks window, providing a "clean" work 
            area.

Open...     Loads a network from disk.

Close       Removes network from the screen and from memory.
            You will receive a warning prompt if you have an
            edited network on the screen.  The network is
            replaced by title screen.

            To load or build a new network you must use the New
            or Open commands from the file menu, or the BackProp
            Builder..., or InstaNet... commands from the Network
            menu.

Save        Saves the network to the previously specified file.

Save As...  Allows you to specify a file name to save your network
            to, and provides a choice of save formats (binary, ascii
            or annotated).

Revert to Saved
            Abandons all edits since the last time the file was 
            saved, and reloads the file from disk.


Print Network...
            Prints network data in a tabular form.  You can 
            print this to a disk file, the printer or to the 
            screen.

            Saves the screen contents as a TIF (Tagged Image
            Format) File.

Current Directory...
            Selects "current" directory. Network files, ASCII data 
            files and User IO programs are read from the current 
            directory.

Help        General Help Information.

Quit        Closes the NWorks application.




Network Menu (see the Network Menu chapter in the Reference Guide)

BackProp Builder...
            Primary mechanism used to build back-propagation
            networks.  Define architecture, learning rules,
            transfer functions, data files, etc.

InstaNet...
            Create a network from a standard library of net-
            works.

Parameters...
            Change various network global parameters.  These 
            include Auto-Associative or Hetero-Associative 
            network type, display mode (Network or Hinton), 
            Control Strategy, Learning and Recall Schedule and 
            Display Style.

Display Style...
            Load, Save, Create or Edit a display style.  This 
            gives you control over the colors and sizes of PEs 
            and connections in the display, as well as the 
            overall scale of the display.

L/R Schedule...
            Load, Save, Create or Edit a Learning and Recall   
            Schedule.

Load Control Strategy...
            Load a disk resident control strategy into memory.

Load Error Function Table...
            Load a non-standard error function table.

Initialize  All Variable weight connections in the network are 
            assigned random values from either a Uniform
            distribution, or a Gaussian distribution, depending
            on the layer specific noise function.  The layer
            dependant Low and High limits are used to define
            the interval for the Uniform distribution, or
            the +/- 1 sigma for the Gaussian. Disabled weights
            are re-enabled.  Delta weights are reset to zero.
            Useful to bring a network back to an initial
            untrained state.
            
            Randomizing weights for a processing element which 
            uses the Kohonen, Kohonen1, KohonenN, or SPR learn-
            ing rule will cause the weights to be multiplica-
            tively post-normalized as appropriate. Delta weights 
            are zeroed.  The Learn Counter is reset to 0.


Purge       This command deletes all disabled connections in the 
            network. Disabled connections are marked with an X 
            through the connection in the network diagram.  
            Connections can be disabled with the Pruning mecha-
            nism (see the Checkpoints command in the Run menu).  
            Connections can be re-enabled with the 
            Network/Initialize command, as well as with the 
            various randomize weights tools.


Randomize Weights...
            Randomize all of the variable weights in the network .
            Values are taken from either a Uniform distribution,
            or a Gaussian distribution, depending on the layer
            specific noise function.  The layer dependant Low and
            High limits are used to define the interval for the
            Uniform distribution, or the +/- 1 sigma for the
            Gaussian. All disabled weights are re-enabled.  Delta
            weights are reset to zero.  The learn counter is set
            to 0.

Jog Weights...
            Slightly perturb all of the variable weights in the 
            network.  Delta weights are reset to zero.


Zero Delta Weights
            Sets to zero all the fields associated with a variable
            weight connection except for the weight field itself.
            For example, Cumulative and Normalized Cumulative
            Back Propagation use extra fields to accumulate weight
            changes and to remember the accumulated weight changes
            at the last update to the weights (for momentum).


Random Number Seed...
            Set the random number seed used by the random number 
            generator.  


Hide Connections
            Hide all of the connections in the network. They may 
            be selectively "shown" using the various Show Con-
            nections tools within the object palette.

Show Connections
            Show all of the connections in the network.


IO Menu (see the "IO Menu" chapter in the Reference Guide)

Parameters...
            Choose the learn source, recall source, and results 
            destination, as well as the options to use binary 
            files or load files into memory.  Also, set epoch 
            size, network ranges and select the MinMax table.

MinMax Table...
            Select or edit a table that specifies the smallest 
            and largest values expected for a given input or 
            output field in real world units.  Tables can be 
            generated quickly from the specified input file 
            using the QUICK GEN button in the IO/Parameters... 
            dialog box.  There is also an option in BackProp
            Builder, and InstaNet (both in the Network menu) to
            generate MinMax Tables.

Rewind Input
            Allows you to rewind or close input data files and
            result files for the currently loaded network.  If
            I/O is being done through a User program, this issues
            a command to the user program UIO_Rewind( ).

Attention I/O
            Sometimes it is necessary to start an interaction
            with a User IO program which is not related to the
            input or output of data. Attention IO allows you to
            do that by calling the UserIO function           
            Uio_Attention( ).



Instrument Menu (see the "Instrument Menu" chapter in the
                 Reference Guide)

Initialize Instruments
            Zero out all data arrays associated with all instru-
            ments.  For strip charts, set the marker position to 
            the start of the chart.
            
Select Probe...    
            The Select Probe command allows you to create or 
            modify probes.  A probe is a means of pointing to 
            groups of processing elements and/or connections for 
            the purpose of later extracting values from them.  

Outline     Temporarily display all instruments in outline mode.

Disable     Temporarily disable display and logging for all 
            instruments.

Enable      Enable all instruments, regardless of their last saved
            state.

Revert      Revert all instruments to their states prior to the 
            temporary outline and disable settings.



Run Menu (see the "Run Menu" chapter in the Reference Guide)


Learn...    NWorks provides five options for controlling the 
            learning process:

            Learn For: Repeat cycle for a specified number of 
            times.

            Learn One Pass/All: Allows the network to learn 
            through as many data items as are in the data file 
            (one complete pass through the file).

            Learn All: Allows the network to learn a single 
            item.

            Learn Now: Assumes that the input layer has the 
            desired inputs and the output layer has the desired 
            outputs.  Performs a single learning cycle.

            Until: Learning proceeds until the learn counter 
            reaches the value in the text field.

Test...     Test provides a way of finding how well your network 
            has learned.  The recall cycle is repeated a given 
            number of times, depending on the option chosen.  
            The desired output must be available either through 
            the Test/Recall file or through the User IO program.

            Test has three options: For, One Pass/All and One.  
            See the options in Learn (above) for an explanation 
            of Test options.  More detailed help is available 
            through the Test dialog box help.

Recall...   Recall is used to actually run data through a 
            trained network.  Recall has four options: For, One 
            Pass/All, One or Now.  See the options in Learn 
            (above) for an explanation of the Recall options.   
            More detailed help is available through the Recall 
            dialog box help.

Recall Start
            Set up the network to do a recall.  Used in conjunc-
            tion with Recall Step to watch networks that iterate 
            internally, such as: Hopfield, Brain-State-in-a-Box, 
            Traveling Salesperson, Bi-directional Associative 
            Memory, ART1, Hamming with lateral inhibition and 
            Recirculation networks converge.

Recall Step 
            Perform a single cycle through the internal recall 
            in an iterative network as described above in Recall 
            Start.  If you are going to use Recall Step, you 
            MUST first do a Recall Start.

Flash Code...
            Convert your trained feed-forward network into a
            "C" code callable function.

Explain...
            Allows you to determine which inputs have the most
            effect on the output in a trained network.


Change Counters...
            Change or inspect the counters which select the 
            appropriate column in the Learning and Recall Sched-
            ule.  Change Auxiliary counters that are network 
            dependent.  

Check Points...
            These allow you to automatically do the following
            during execution: update the display, save the
            display as a graphics file, or save the network.
            The pruning facility and weight limiting facility,
            and the DNNA discretization mechanism are also 
            accessible from this submenu.

:file@currentdirectory
             --- Help about File/Current Directory ---

Selects "current" directory. Network files, ASCII data files
and User IO programs are read from the current directory.
:file@oscall
            --- Help about File/OS Call ---

Jump to DOS.  Allows you to execute DOS commands without
quitting NWorks.  Type EXIT to return to NWorks.
:file@quit
            --- Help about File/Quit ---

Closes the NWorks application.


:network@backpropbuilder
          --- Help about Network/BackProp Builder ---

BackProp Builder is the primary mechanism you will use to develop
back-propagation networks.  It provides a dialog box with
numerous choices that allows you to construct thousands of
variations of back-propagation networks, yet for all of its
flexibility it makes constructing typical back-propagation
networks extremely simple.

To build a back-propagation network with BackProp Builder, you
need to specify at least the following:

The number of PEs per layer in the network (set through the text
entry fields under # PEs).

The Learning Rule (selected in the Learn Rule scroll window).

The Transfer Function (selected in the Transfer scroll window).

The Learn, or training, data file (selected in the Learn 
scroll window).

The Recall/Test data files (selected in the Rcl/Test scroll 
window).

In addition, you can customize your network or add instruments
through the check boxes, change the learning coefficients and
their decay characteristics, set a momentum term and generate
MinMax tables to scale the data from you training file and test
files.  

The Using NWorks tutorial covers using BackProp Builder in 
detail.

The following lists all of the dialog box components and their
functions:

# PEs 

These text fields specify the number of processing elements
(nodes) for each layer in the back-propagation network.  Input
corresponds to the input or bottom layer, Hid 1 through Hid 3
correspond to three hidden layers (usually you will only need
one or two hidden layers), and Output corresponds to the output
or top layer.

The Building the Network section in the "Using NWorks"
tutorial provides some guidelines for selecting the number of
PEs in a hidden layer.  The number of PEs in the input and 
output layers depend on the number of data fields in each data
vector in your training data.  The number of outputs depends
n what information you want your network to provide (and
requires a matching number of data fields for desired output).

The Adding Layers to a Network section in the "Using NWorks"
tutorial provides some guidelines for selecting the number of
hidden layers in your back-propagation network.

LCoef

The LCoef fields correspond to learning Coefficient #1 for
each of the hidden layers and the output layer.  Learning
coefficients are used by the learning and recall schedule, and
(if you the Default Schedule box is not checked) BackProp
Builder constructs a separate learning and recall schedule for
each hidden layer and the output layer.  See the Reference Guide
or a detailed description of learning and recall schedules.

If you select the Default Schedule check box, BackProp 
Builder will ignore the LCoef, Momentum, 1st Trans. Pt. and
LCoef ratio fields, and will instead set each layer to use the
standard backprop global learning and recall schedule.

LCoef works in conjunction with the 1st Trans. Pt. and LCoef 
ratio values to configure the learning and recall schedules.
The value entered in a layer's LCoef field corresponds to the
first Coefficient 1 value in the learning and recall schedule
(shown in the following figure).  The 1st Trans. Point corresponds
to the learn count value set in column 1 in the schedule.  The
learn count for the subsequent columns are heuristically set to
3, 7, 15 and 31 times the learn count you enter in the 1st Trans.
Point field; i.e., the intervals between transition points
increase exponentially.  The LCoef ratio sets the the amount to
by which to divide the LCoef value for the first transition.
This defines an exponential decay which is sampled at subsequent
transition points.  For example, if you set a learning
coefficient of 0.5 and an LCoef ratio of 0.5, the values for the
various columns in the schedule will be:

Column 1 = 0.5 (the LCoef value)
Column 2 = 0.25 (the previous column value divided by the
                 LCoef ratio value of 2)
Column 3 = 0.0625 (the previous column value divided by 4)
Column 4 = 0.00391 (the previous column value divided by 16)
Column 5 = 0.00002 (the previous column value divided by 256)


Momentum

The Momentum field value is also used in configuring the
learning and recall schedules for the hidden and output layers.
Refer to the Back-propagation chapter in the Neural Computing
Guide.

For back-propagation networks, momentum is represented in the
learning and recall schedules by learning Coefficient #2.  The
Momentum value interacts with the 1st Trans. Pt. and LCoef
Ratio exactly as do the LCoef field values described above.

1st Trans. Pt. 

See the explanation in the LCoef section above.

LCoef Ratio

See the explanation in the LCoef section above.

Learn Rule

The Learn Rule scroll window allows you to select the learning
rule that is applied to all layers in the back-propagation
network.  The learning rule specifies how connection weights
are changed during the learning process.  The three learning
rules available are:

Delta-Rule

Cumulative Delta-Rule

Normalized-Cumulative Delta-Rule

You can use the Layer/Edit tool to assign learning rules on a
layer-by-layer basis.

These learning rules are described in detail in the Processing
Element chapter of the Reference Guide.  For most applications,
we recommend trying the normalized-cumulative delta-rule 
(or with fast learning, the delta-rule).

Transfer

The transfer function scroll window allows you to specify a
transfer function that is used for all layers in the network.
The transfer function is a non-linear function that transfers
the internally generated sum for each PE to a potential output
value.  Available transfer functions are:

Linear

Sigmoid

Hyperbolic tangent (TanH)

Sine

These transfer functions are described in the Processing Element
chapter of the Reference Guide.


Learn 

The Learn scroll window is used to select the training data file
for the network.  Only .nna files resident in the current
directory are shown.  The current directory can be changed using
the Current Folder or Current Directory command.  Alternatively,
you can type the file name into the text entry field.


Rcl/Test

The Rcl/Test scroll window allows you to select a data file for
recall and test execution.  Again, only .nna files resident in
the current directory are shown.  The current directory can be
changed using the Current Folder or Current Directory command.
Alternatively, you can type the file name into the text entry
field.


Connect Bias 

Selecting this check box connects the Bias PE to each hidden and
output layer. The Bias PE is analogous to a ground in an
electrical circuit in that it provides a constant input value.


Connect Prior

Connect prior fully connects the input layer to all hidden layers 
and the output layer.  This provides an alternative architecture
(the sample network is an example of a connect prior back
propagation network).


Functional Links

Checking this box will create a back-propagation network with a
functional link layer above the input layer.  The network
computes the second order iterations by creating a special layer
which computes the pair-wise product of each of the inputs. This
special layer is considered as an extra input layer and is
connected to the hidden and output layers in exactly the same way
as the input layer.

A second functional link variation of back-propagation is 
available through the InstaNet facility.

This configuration can sometimes improve the learning rate,
and is discussed in detail in the Functional-link Networks
(FLN) chapter in the Neural Computing guide.


Auto-Associative

If Auto-Associative is checked, BackProp Builder sets the number
of ouput PEs to the number of input PEs and, when training, uses
the input data as the desired output.  Back-propagation networks
can use this mode for applications such as data compression or
noise filtering.


Linear Output

Linear Output overrides the selected transfer function and forces 
a linear transfer function for the output layer.  The linear
transfer function takes the current sum for each PE as its output.


Gaussian Noise

Attaches the Gaussian noise function (instead of the uniform noise 
function) to all layers in the network.  Three things must occur
before a layer actually uses the noise function:

The control strategy must call for a noise function (the 
backprop control strategy does).

The learn and/or recall temperature value in the learning 
and recall schedule must be set to a non-zero value.  By default, 
BackProp builder sets these to zero.  Recall temperature
is explained in the chapter on Laerning/Recall Schedules in the
Reference Guide.

A noise function must be attached to the layer.

Uniform noise adds a random number within a specified range to
each PE summation value in the layer.  The range for random
numbers being plus or minus one percent of the temperature
value.  The random number for the noise value is different for
each PE in the layer.  Gaussian noise is similar to Uniform
noise, except that the distribution of random numbers within
the range is along a bell curve; i.e., more concentrated
toward the middle of the range than at the ends.


Tolerant Error

Tolerant error uses the learn Coefficient 3 value as an error
value to be considered as zero (when this value is reached
learning stops).  You must use the Layer/LRS tool to change
this value for the output layer.  When creating learning and
recall schedules, BackProp Builder sets Coefficient 3 to a
default value of 0.1.


Fast Learning

Selecting this check box uses a fast version of the back
propagation control strategy.  We also recommend that you use
the delta-rule learning rule for fast learning.  See the
section on Learning Rules in the chapter on Processing Element
Architecture in the Reference Guide for more information.


Minimal Config.

Minimal Config. provides the minimum number of weight fields
required for a learning rule.  For instance, a minimum
configuration of the cumulative delta rule will have two weight
fields.  Not checking this would provide the cumulative
delta-rule with three weight fields, the third being used for
momentum.

You should check this box if your computer system does not have
enough memory for the default configuration.


Default Schedule

Checking this box causes the network to use the default learning
and recall schedule (backprop) for all layers.  Not checking
this box creates a separate learning and recall schedule for each
layer.


Default IO Files  

Selecting this check box causes the network to use default IO
file names instead of those selected through the scroll window.
The default IO file names are train for the Learn file, and test
for the Rcl/Test file.

These file names are set in the backprop.ins InstaNet file.
Advanced users can edit this file to change these default names.
Refer to the InstaNet Description Language chapter for a
reference to this language.


MinMax Table

Selecting this check box causes NWorks to compute the low and 
high values for each data field in the selected data files,
and stores these in a MinMax Table.  When data is presented to
the network, it is scaled to the network ranges using the MinMax
table and the network range values (set through the
IO/Parameters command).  MinMax tables and network ranges are
discussed in the MinMax Tables chapter of the Reference Guide.


RMS Error Graph

Choosing this check box creates a strip chart instrument that
shows the RMS error of the output layer.  As learning progresses,
you should see this graph slowly converge to an error near zero.
When the RMS error equals the convergence threshold value,
training will ceases.  By editing the instrument (use the
Graph/Edit tool), you can chage the convergence threshold value
(or deactivate it). BackProp Builder sets the convergence
threshold to 0.001 by default.


Weight Histogram

Checking this box creates a normalized histogram of all variable
weights in the network.


Confusion Matrix

Selecting this box constructs a confusion matrix instrument for
each of the output PEs (up to a maximum of four confusion
matrices).  Refer to the Graph Palette chapter in the Reference
guide for a description of the confusion matrix instrument.


:network@instanet
                  --- Help about InstaNet ---

InstaNet is the primary mechanism you will use to develop all
networks other than back-propagation networks. You also can use
the Add and Clone tools found in the object palette to build
networks from scratch.  If you are using these tools to build
networks, and you find that you are repeating the same steps
for each network that you build, you can incorporate these steps
in a custom InstaNet script which will then appear in the
InstaNet dialog box scroll window.

InstaNet allows you to load a generic network type and easily
specify its attributes to suit your purpose. You may specify 
the number of layers and the number of processing elements in
each layer. The Input, Hidden 1, Hidden 2, Hidden 3 and Output
fields allow you to enter the number of PEs for each of these
layers. Refer to the chapter in Neural Computing for each network
type for specific information concerning how these layers are
constructed for that network type.  

InstaNet will also help you by adjusting the number of processing 
elements in a layer where required, such as when the input and
output layer must have the same number of processing elements and
the user has mistakenly specified otherwise.  Note that not all
fields are used for every type of network.  InstaNet will ignore
field values where they are not needed and will prompt you with
an error message when you provide inconsistent values for a given
network type.

Proceed as follows to construct an InstaNet:

From the Network scroll window, select the standard network you
would like to use.

In the # of PEs column, specify the number of processing elements
you would like in each layer.  To avoid building a layer, specify
zero for that layer.

If Data I/O will be done in file mode, select the data files for
learning and for recall/test.  You can do this by either selecting
them through the Learn and Rcl/Test (recall/test) scroll boxes, or
by typing the file names into the text fields.

Checking Default I/O Files ignores the file names selected and 
uses default file names.  Advanced users can change the default
file names by editing the corresponding InstaNet script file.

RMS Error Graph, Weight Histogram and Confusion Matrix instruments
are available (and are the same as those listed under the BackProp
Builder command).  Pick the instruments you want by selecting 
the check box.  Note that not all instruments are valid for all 
network types.

Selecting the MinMax Table check box causes NWorks to compute the
low and high values for each data field in the selected data
files, and stores these in a MinMax Table.  When data is presented
to the network, it is scaled to the network ranges using the
MinMax table and the network range values (set through the
IO/Parameters command).  MinMax tables and network ranges are
discussed in the MinMax Tables chapter of the Reference Guide.

Selecting the Minimal Configuration check box will save you memory
by reducing the number of weight fields to the minimum.  For 
example, many learning rules (such as Kohonen) use, by default,
two weight fields per connection: one for varying weights and one
for the delta weights.  Such learning rules may not require, or
may only optionally require, the delta weight field.  In such
situations, selecting Minimal Configuration will allocate only
one weight field per connection, thus significantly reducing
required memory.

The names which appear in the scroll window are actually script
files which tell NWorks how to create the desired network. Some
of the main network types supported by InstaNet are described
below.


InstaNet Adaline Network

A single-layer network invented by Bernard Widrow. Though this
network suffers from several of the drawbacks of the perceptron,
it has been applied to a number of practical problems with
interesting results.

1.   Select adaline from the scroll window.

2.   Enter the number of input and output processing elements.

The adaline layer will contain the same number of PEs as specified 
in the output layer.


InstaNet Art1 (Adaptive Resonance Theory 1) Network

A binary version of Carpenter and Grossberg's adaptive resonance
theory for pattern recognition.

ART1 is protected by a patent held by the University of Boston.
To receive a disk with the ART 1 InstaNet and Control Strategy
files for NWorks contact:

     Boston University Patent Administrator,
     Boston University Community Tech. Foundation,
     881 Commonwealth Ave.,
     Boston, MA 02215.
 
     Phone: (617) 353-2212. 

1.   Select art1 from the scroll window.

2.   Set the number of input (F1 layer) and output (F2 layer) 
     processing elements.


InstaNet Back-Propagation Networks

There are two varieties of back-propagation networks included
in the InstaNet menu (although you should use BackProp Builder
as your primary tool for constructing back-propagation networks).
These are listed and described below. For more information, refer
to the Back-propagation chapter in the Neural Computing volume.

Functional Link Network

NWorks includes two variations of the Functional Link network, 
one of which is available through BackProp Builder and the other
through InstaNet.  

This variation on Functional Link networks augments the first
order statistics with sine functions. In this InstaNet, hidden 1
is the number of sine layers to generate.

1.   Select bkpfuncs.

2.   Set the number of input elements, sine layers in hidden 
     layer 1, number of elements in the hidden layer in hidden 2
     (if desired), and output elements.

Back-Propagation Network with Pruning

One or two hidden layers and an output layer each use the
cumulative generalized delta rule. The number of training
intervals between weight updates is set in the Aux 1 counter.
Momentum does not apply. Pruning is activated.  All PEs in the
Hidden 1 layer have connections from previous PEs in that layer.

1.   Select bkpprune.

2.   Set the number of input, hidden layer 1, hidden layer 2
     (optional) and output processing elements.


InstaNet BAM (Bi-directional Associative Memory) Network

A network which was inspired by the adaptive resonance theory
and generalizes the Hopfield network for hetero-associative
recall. 

1.   Select bam from the scroll window.

2.   Set the number of input (BAM 1 layer) and output (BAM 2
     layer) processing elements.


InstaNet Boltzmann Network 

A symmetric binary network which uses a stochastic transfer
function together with an annealing schedule based on the
Boltzmann distribution to perform a recall. Two versions are
provided. For the completion model (boltzcmp), learning is driven
by comparing the difference in the states of the network with
visible units clamped and states when they are unclamped. For the
IO version (boltzio), the input units are always clamped and the
learning is driven by comparing the difference in the state of the
network with output units clamped and its state with output units
unclamped. 

1.    Select boltzcmp or boltzio.

2.    Set the number of input, hidden 1, and output (IO model
      only) processing elements.


InstaNet BSB (Brain-State-in-a-Box) Network

A linear associative network with a non-linear post-processing
algorithm in recall. This network has been studied as a model of
human cognition.  There are two variations of this network
available: One with Hebbian learning (bsbhebb) and another with
Widrow-Hoff (bsbwh). 

1.    Select either bsbhebb or bsbwh.

2.    Set the number of input processing elements. 

Since these are auto-associative networks, the number of
processing elements in the hidden and output layer are the same.


InstaNet Cascade-Correlation Network

Cascade-Correlation networks were suggested by Scott Fahlman and
Christian Lebiere.  In cascade-correlation networks, PEs in the
hidden layer are sequentially added.  New hidden layer PEs are
first trained to the output error of the current network. Then
they are used as input to the output layer and the output layer
is retrained. At each step only one layer is trained. This
sequence is then repeated as many times as needed.

1.    To create a cascade-correlation network, select cascade.

2.    Set the number number of input, hidden layer and output 
      PEs.  You cannot exceed seven hidden layer PEs.

Use whatever learning and recall schedule is appropriate at each
point of the Cascade-Correlation training process.  When using
InstaNet, the Cascade-Correlation network learning rule selected
is norm-cum-delta.  This may be changed at any point in the
process. Generally, some variant of back-propagation should be
used.


InstaNet Category Learning Network

This is a three layer network which uses a middle Kohonen layer
to competitively select among several possible categories. The
network is useful for pattern-classifying expert systems, and
shares many characteristics with counter-propagation below.
However, it assumes that the outputs of the network will always be
zero or one. 

1.    To create a category learning network, select catlrnn.

2.    Set the number of input, hidden layer 1 (competitive 
      layer size), and output processing elements. 

The output layer is connected to the competitive layer through
fixed weights. These are distributed among the output units in
a round-robin fashion.

InstaNet Counter-Propagation Network

A three-layer network which implements the unidirectional
variant of a network invented by Robert Hecht-Nielsen. This
network is useful for pattern classifying expert systems. 

1.    To create a counter-propagation network, select cntrprp. 

2.    Set the number of input, hidden layer 1 (Kohonen layer 
      size), and output processing elements.


InstaNet Delta-Bar-Delta and Extended Delta-Bar-Delta Networks

Delta-Bar-Delta networks were suggested by R. A. Jacobs, and
attempt to improve convergence speed via a heuristic route.
By using past values of the gradient, heuristics can be applied
to infer the curvature of the local error surface. With this
type of information, intelligent steps can be taken in the
weight space using a number of straightforward rules.

Extended delta-bar-delta networks were proposed by Minai and
Williams.  They incorporate momentum adjustment, based on
heuristics, in an attempt to increase the rate of learning.
Several other modifications to the delta-bar-delta technique are
included in their algorithm, called the Extended-Delta-Bar-Delta
(EDBD) learning rule.

1.    To create a delta-bar-delta network, select dbd.  To 
      create an extended delta-bar-delta network, select edbd.

2.    Set the number of input, hidden layer, and output
      processing elements.


InstaNet Digital Neural Network Architecture (DNNA) networks

Digital Neural Network Architecture was developed by engineers
at Neural Semiconductor, Inc. The key to the architecture is the
efficient implementation of a sigmoid-like activation function
that can be built with standard digital hardware.  High densities
of connections per silicon area have been achieved, exceeding
analog implementations, and the chips can be produced with 
standard, low-cost semiconductor fabrication processes.

DNNA InstaNet files are: dnnabkp, which uses the delta-rule 
learning rule; dnnacum, which uses the cumulative delta-rule 
learning rule; dnnancum, which uses the normalized-cumulative 
delta-rule; and dnnasamp, which creates a network to solve the 
exclusive-OR problem.

1.    To create a DNNA network, select dnnabkp, dnnacum, 
      dnnancum or dnnasamp. 

2.    Set the number of input, hidden layer, and output
      processing elements.


InstaNet Directed Random Search (DRS) Network

The DRS technique is an extension of the Random Optimization
Method of Matyas, which was developed in the context of standard
functional optimization. Random steps are taken in the search
space. At each step, the function to be minimized is evaluated.
Over a bounded set in weight space, the Random Optimization
Method is guaranteed to converge to the global minimum value with
a probability of one.

Our implementation includes a self-adjusting variance step size not 
found in the basic Random Optimization method, which often speed
convergence.  However, in some instances, the method can become
trapped in local minimum if the variance becomes too small.

1.    To create a DRS network, select drs.

2.    Set the number of input, hidden layer, and output
      processing elements.


InstaNet Hamming Network

A neural implementation of a minimum error classifier using
hamming distance. Two versions are provided: hamlatin which
uses lateral inhibition to implement the competition, and
hamming which is much faster and uses the NWorks competitive
output function. Hamming networks can be used to do simple
inary pattern matching. 

1.  Select hamlatin or hamming from the scroll window.

2.  Set the number of input and output processing elements.


InstaNet Hopfield Network

This creates a single-layer network with feedback to itself. This
network was originally invented by Stephen Grossberg and became
famous through the work of John Hopfield. 

1.    Select hopfield from the scroll window.

2.    Set the number of input processing elements. 

Since this is an auto-associative network, is assumes that there
are the same number of processing elements in the hidden and
output layers as there are in the input layer.


InstaNet Learning Vector Quantization (LVQ) Networks

Learning Vector Quantization is a classification network,
originally suggested by T. Kohonen, which assigns vectors to
one of several classes. An LVQ network contains a Kohonen layer
which learns and performs the classification.  LVQ provides equal
numbers of PEs for each class in the Kohonen layer.

1.    Select lvq from the scroll window.

2.    Set the number of input, hidden layer 1 (adalines), and
      output processing elements.

Hidden layer 1 PEs equal the number of PEs assigned to each 
class within the hidden layer.

Output layer PEs should be set to the number of classes. 


InstaNet Madaline Network

A three-layer network which combines multiple adalines together.
This is a very interesting and powerful network for classifying
patterns which can be mapped into binary inputs. 

1.    Select madaline from the scroll window. 

2.    Set the number of input, hidden layer 1 (adalines), 
      and output processing elements. 


InstaNet Perceptron Network

A simple three-layer, feed-forward network invented by Frank
Rosenblatt. It is included for its historical significance more
than its practical applicability. However, several interesting
experiments have been done with it. 

1.    Select percptrn in the scroll window. 

2.    Set the number of input and output processing elements. 

3.    If you want a daemon layer, set the number of elements in
      this layer in the hidden layer 1 field.


InstaNet PNN (Probabilistic Neural) Network

This network was proposed by Donald Specht. PNN is a neural
implementation of a Bayes classifier where the class probability
density functions are Parzen estimators with Gaussian kernels. 

1.    Select pnn.

2.    Set the number of input, hidden 1 (prototypes), and output
      (classes) processing elements.


InstaNet Recirculation Network

An encoder network, similar in function to auto-associative
back-propagation, but using only feed forward connections. This
was proposed by Geoffry Hinton and James McClelland. 

1.    Select recirc in the scroll window.

2.    Set number of input and hidden layer 1 processing elements.


InstaNet Sample (Two-Input Back-Propagation) Network 

This script file generates a two input back-propagation network
with a single element in the hidden layer. 

1.    To create this network, select sample in the scroll window. 

You need not enter anything into any of the other fields. 


There is also a version of this InstaNet script (samplei) which 
creates the samplei network with RMS error and weight instruments 
and a weight histogram. 


InstaNet Self-Organizing Map (SOM) Networks

The self-organizing map neural network sorts items into
appropriate categories of similar objects by creating a
two-dimensional feature map of the input data in such a way
that order is preserved. The Kohonen layer, which is the heart of
the SOM network, has variations that can be used for optimization
problems.

Three SOM InstaNets are provided: som, the standard SOM network; 
som_cat, the SOM network with categorization; and som_bkp, the SOM
network with prediction.

To create a standard SOM network:

1.    Select som.

2.    Set the number of input PEs to the number of variables 
      in the input data.

3.    Set the number of Hidden 1 layer PEs equal to the number
      of rows in the two-dimensional Kohonen layer.

4.    Set the number of Hidden 2 layer PEs equal to the number 
      of columns in the two-dimensional Kohonen layer.

To create a SOM network with categorization:

1.    Select som_cat.

2.    Set the number of input PEs to the number of variables 
      in the input data.

3.    Set the number of Hidden 1 layer PEs equal to the number 
      of rows in the two-dimensional Kohonen layer.

4.    Set the number of Hidden 2 layer PEs equal to the number 
      of columns in the two-dimensional Kohonen layer.

5.    Set the number of Output layer PEs to the number of 
      categories.

To create a SOM network with prediction:

1.    Select som_bp.

2.    Set the number of input PEs to the number of variables 
      in the input data.

3.    Set the number of Hidden 1 layer PEs equal to the number 
      of rows in the two-dimensional Kohonen layer.

4.    Set the number of Hidden 2 layer PEs equal to the number 
      of columns in the two-dimensional Kohonen layer.

5.    Set the number of Hidden 3 layer PEs equal to the number 
      of PEs in the prediction hidden layer.

6.    Set the number of Output layer PEs to the number of 
      prediction variables.


InstaNet SPR (Spatio-Temporal Pattern Recognition) Network

This is a variant of a network invented by Stephen Grossberg. As
the name implies, it is useful for temporal or spatial pattern
matching. 


1.    Select spr.

2.    Enter the number of inputs in the input processing 
      element field. 

If the number in the hidden layer 1 field is non-zero, a 
normalizing layer will be created to preprocess the input data. 

3.    Set the number of SPR layers in the Hidden 2 field.

The number in the hidden layer 2 field tells the SPR network 
builder how many SPR layers to create. There should be one SPR
layer for each time slice required for recognition. 

4.    Set the number of output processing elements equal 
      to the number of classes to recognize.


:network@parameters
           --- Help about Network/Parameters ---

Use this command to change many of the key parameters which
control the operation of the network.  These are described
below.

Title
            The title may be longer than the dialog box.  It
            appears at the top left of the network.


Network Type
            Auto-Associative (input=output) or Hetero-Associa-
            tive.  If auto-associative is chosen for a network
            which requires desired output, the same data is
            presented for desired output as was presented at
            the input layer.

Display Mode
            Display as a Network diagram or a Hinton diagram 
            (all PEs shown along the x and y axis, with their 
            intersection on the display showing the connection 
            strength).

Control Strategy
            Selects the control strategy in memory that controls 
            the flow of information through the network.  (To 
            load a control strategy into memory, use the Net-
            work/Control Strategy command.)  

L/R Schedule
            Selects the Network Global Learning/Recall Schedule.
            (See the Network / L/R Schedule command to load, 
            edit or save an L/R schedule.  To attach an L/R 
            schedule to a layer, see the Layer/LRS tool.)
Display Style
            Selects a set of parameters which control the dis-
            play of a network.  (See the Network/Display Style 
            command to load, edit or save a display style.)

:network@displaystyle
         --- Help about Network/Display Style ---

This command changes the way in which the network is displayed by 
modifying the current network style sheet.   The action buttons 
and text field let you:

LOAD        Load other style sheets from disk

SAVE        Save style sheets to disk

Name        Rename a style sheet

EDIT        Edit another style sheet currently resident in 
            memory 

CLEAR       Erase a style sheet from memory 

NEW         Create a new style sheet

One of the most useful fields in the display style sheet is the X 
and Y scale.  Changing these values allows you to shrink or 
expand a displayed network in the X and Y direction.

The threshold points for processing element and connection displays 
may be set.  Colors for titles, background, processing elements and 
connections can also be set.  All connections can be shown as 
Solid. The size of the PEs can remain Fixed.

To select a new color, simply click the color in the color palette.
A reverse video cross will show your selection.

You can change the Size of the PE input and output terminal, as well 
as the orientation angle of the terminal.

Checking the Hopfield box will display a connection matrix below a 
layer that has feedback connections to itself.  Make sure there is 
room under the layer for this matrix to be displayed.  Otherwise, 
lower layers may be overlaid.

:network@l/rschedule
            --- Help about Network / L/R Schedule ---

Learning / Recall Schedules allow the various parameters for 
controlling the learning and recall process to be changed as a
Learn or Recall progresses.  These are set up as named tables
which can be selected for each layer or as a network global
schedule.

This dialog box allows you to LOAD, SAVE, EDIT, CLEAR, and
create (NEW) Learning and Recall Schedules.  You select the
applicable schedule in the Network/Parameters command dialog
box (for the network global schedule), and the Layer/Edit tool
dialog box to select a schedule for a particular layer.

There are five columns available for both learning and recall.
The column used depends on the current value of the Learn and
Recall Counter.  A given column is used until the applicable
learn or recall count exceeds the value specified at the top
of the respective column.

The meanings of the various Learning Coefficients, Temperature 
values, etc. are network dependent.  Refer to the "Network Menu" 
chapter, "L/R Schedule" section in the Reference Guide for specific 
information.

:network@loadcontrolstrategy
        --- Help about Network/Load Control Strategy ---

NeuralWorks Professional II allows you to load control strategies
from disk. This dialog box shows you a list of disk resident 
control strategies. 

  1.Select a strategy in the scroll window.

  2.Click the OK button.

You can display the control strategies in memory through the 
Network/Parameters command. The currently memory resident control 
strategies are shown in the lower left corner of the display.

If you load a control strategy with the same name as one already 
in memory, the one in memory will be over-written. You cannot 
load over one of the standard memory-resident control strategies. 
For NeuralWorks Professional II, there is only one standard 
memory-resident control strategy. This is backprop; all the other 
standard control strategies are disk resident. For NeuralWorks
Explorer, all supported control strategies are memory resident,
and you cannot load control strategies.

NWorks first checks in the current folder to see if there are 
any Control Strategy (.nnc) Files, and then checks the NWorks 
folder.  See the Options command (Edit menu) to change the cur-
rent and the NWorks folders.

When a control strategy is loaded, it automatically becomes the 
network control strategy. You can verify this by looking at the 
Control Strategy scroll window in the Network/Parameters dialog 
box. 

Refer to the Control Strategies and User-Defined Control Strate-
gies chapters in the NeuralWorks Reference Guide for more infor-
mation.
:network@loaderrorfunctiontable
      --- Help about Network/Load Error Function Table ---

The error function table command allows you to create and use a 
special table which defines the error for any pair of inputs 
(Desired Output, Actual Output).  The error may be multiplied by 
the table output or use the raw table output.  The table must be
a 10 x 10 matrix.  Dual interpolation is used to approximate the
value of the surface defined by the matrix and provide a smooth
set of values over the range for Desired Output and Actual
Output.  

When a table is loaded, it will be displayed (and can be select-
ed) in the Layer/Edit tool dialog box in the Error scroll window.  

At this time, there is no mechanism to delete a table from memory.
:network@initialize
          --- Help about Network/Initialize ---

Initialize  randomizes incoming variable weights to each layer.
Either a Uniform or Gaussian distribution is used depending on
the selected noise function for a given layer.  The Init Low and
Init High limits for each layer are used to define the interval
for the Uniform distribution or the +/- 1 sigma if the noise
function is Gaussian.  Set the Init Low and Init High values to
be the same if you want to set the incoming weights to a layer
to a constant value.  If the layer has a Kohonen, Kohonen1,
KohonenN or SPR learning rule, the weight vector will be
multiplicatively post-normalized.

A number of other things happen when you initialize:

1.  The layer counter is reset to 0.

2.  Delta and auxiliary weight fields are set to 0.0.

3   Disabled weights in the network are re-enabled.

4.  Calls are made to any user-defined math function
    initialization routines if these exist as part of
    User-Defined Neuro-Dynamics.
    
5.  All instruments are initialized.
:network@purge
          --- Help about Network/Purge---

Purge deletes all connections in the network that have been
disabled.  Weights are typically disabled with the pruning
mechanism which is accessible in the Check Points command in
the Run menu.
:network@randomizeweights
      --- Help about Network/Randomize Weights ---

Note: The preferred method for randomizing the weights in a
network is to use the Network/Initialize function.  Weights
can also be randomized or initialized on a selective basis
using the tool palette.

Use this command to Randomize all of the variable weights in the 
network (this does not affect fixed, set and modified weights).  
The values selected for the weights are chosen at random from
a Uniform or Gaussian distribution depending on the noise function
chosen for each layer.  This command brings up a dialog box with
High Limit and Low Limit entry boxes.  The values of these limits
define the interval for the uniform distribution and +/- 1 sigma
for the Gaussian distribution.  To set the weights to a constant
value, set the high and low limits both to be that value.

When the weights are randomized, all Delta Weights and auxiliary
weight fields are set to zero.  The Learn counter is reset to 0. 
All disabled weights in the network are re-enabled.  All
instruments are initialized.  Randomizing weights for a processing
element which uses the Kohonen, Kohonen1, KohonenN or SPR learning
rule will cause the weights to be multiplicatively post-normalized
as appropriate.  Fixed, set and modified weights are not
normalized.

The preferred method for initializing networks is to use the 
Network/Initialize command which differs from this function
in that Initialize uses limits set and stored on a per layer
basis;  also, for users with the User-Defined Neuro-Dynamics
extension package, a call is made to each user defined function
on a per layer basis.
:network@jogweights
             --- Help about Network/Jog Weights ---

This command will jog (add a random number) to all of the variable
weights in the network.  The number added to each weight is 
randomly chosen from a Uniform or Gaussian distribution depending
on the noise function chosen for each layer.  The low and high
limits which can be set in the dialog define the interval for
the Uniform distribution, or the +/- 1 sigma for the Gaussian
distribution. When weights are jogged, the Delta Weights are set
to zero.

Jogging weights for a processing element which uses the Kohonen, 
Kohonen1, KohonenN or SPR learning rule will cause the weights to 
be multiplicatively post-normalized as appropriate.

Sometimes when a back-propagation network gets "stuck", it is 
helpful to jog the weights to push the network out of the current
local minima.
:network@zerodeltaweights
        --- Help about Network/Zero Delta Weights ---

Every time a weight change is calculated, the change in weight
is saved in the connection as a "delta weight".  Some learning
rules such as Cumulative and Normalized Cumulative Delta Rule
use the delta weight field and other auxiliary weight fields
to accumulate information prior to updating the weights.  This
command sets the delta weight and auxiliary weight fields to
zero for all variable weight connections in the network.

Zeroing delta weights can be done on a selective basis through
the tool palette.
:network@randomnumberseed
          --- Help about Network/Random Number Seed ---

Set the random number seed used by the random number generator.  
The program uses a standard random number generator.  By setting 
the seed to a known value prior to randomizing the network, you 
can be assured of getting the same randomization each time.  This 
is useful to assist in repeating experiments with different 
learning rates or other parameters.  

The dialog box is provided for selecting a new seed.  If you only 
need to re-initialize the random number generator without chang-
ing the seed, simply click OK.
:network@hideconnections
          --- Help about Network/Hide Connections ---

Hides all connections within the network. Hiding Connections ca
reduce the amount of time required to redraw the network.
:network@showconnections
          --- Help about Network/Show Connections ---
Displays all the connections previously hidden either with this
command or through any of the hide connections tools.

Show connections is not simply a hide connections toggle since
you can show or hide arbitrary sets of connections through the
tool palette.
:i/o@parameters
                --- Help about IO/Parameters ---

This dialog box specifies:

  * The source and destination of network data
  * Result file options (when using Test mode)
  * The User IO file to use
  * The epoch size
  * How to preprocess data so that the input and output values 
     are appropriate for the neural network being used.

The dialog box also provides the QUICK GEN button for quickly 
generating MinMax tables.

Lrn         For the learn source, you can choose the Keyboard, a
            specified User IO program, or File format. The File 
            format mode allows input data to be presented se-
            quentially or randomly.  To speed up IO, a binary 
            file can be created from the ASCII file.  If you 
            request binary and a current binary file already 
            exists, it will be used (otherwise a binary file 
            will be created and used).  You also have the option 
            to load the data file into memory.

            To select the learn source data file, click either 
            the NNA (ASCII) or NNB (binary) radio button beside 
            Learn.  You can then either click on the file you 
            want in the Select Files scroll box, or enter the 
            file name in the Learn text field.

Rcl/Test    The recall or test source can be the Keyboard, a 
            specified User IO program or an ASCII or binary file 
            read sequentially.  The results file can optionally 
            contain the input and/or desired output for compari-
            son purposes.

MinMax Table
            The MinMax Table is a file that specifies the small-
            est and largest values expected for a given input or 
            output field in real world units. It can be generat-
            ed quickly from the specified input file with the 
            QUICK GEN button.  Use the MinMax Tables command in 
            the IO menu to load, save, create and edit MinMax 
            Tables.  When QUICK GEN is used, it will count the 
            number of records in the training set and insert 
            this into the "epoch" variable (Aux1).

File Layout 
            This specifies the start field for the input and 
            output fields in the input file.  This allows the 
            output fields to occur before the input fields in 
            the input file if desired.

Network Ranges 
            This specifies the low values and the high values 
            for the input and output values presented to the 
            network.  The real world values from the MinMax 
            table and the network values specified in Network 
            Ranges are used to create scale and offset values to 
            bring real world values into network range for 
            neural net processing, and then to bring the network 
            range values back to real world values.

            Note that data from User IO programs can also take 
            advantage of the scales and offsets created from 
            MinMax Tables and Network Ranges.
:i/o@minmaxtable
                ---Help about IO/MinMax Table ---

This command allows you to Create, Edit, Load, Save, and Clear 
from memory MinMax Tables.  A MinMax Table is a file that con-
tains information about your input and output data.  Each field 
in a MinMax table contains the actual or expected low and high 
field values.  The MinMax table is used in conjunction with the 
Network Ranges as specified in the IO/Parameters command dialog 
box to take your real world valued input and scale it to ranges 
suitable for your neural network. The results of the network are 
then rescaled back to real world values.

MinMax Source is the name of a data file that can be scanned to 
determine expected or actual low and high ranges for each MinMax 
table data field.  You may display source files with various 
extensions by using the data entry box labeled Requested, and 
then using the SHOW EXTENSION button.

Create a new MinMax table with the NEW button, or edit an existing
MinMax table with the EDIT button.  Use the MERGE TO TABLE button
and the specified MinMax Source file to create or update the
entries in a MinMax Table.

See the "MinMax Tables" chapter, and the "IO Menu" chapter,
"MinMax Table" section in the Reference Guide for more information.
:i/o@rewindinput
              --- Help about I/O / Attention IO ---

This command provides options to rewind or close the learn file,
the recall/test file or both.  Closing these files useful in a
multi-windowing environment to allow you to edit them without
having to save your network and exit from NWorks.
            
If you rewind or close the recall/test file, the result file is
automatically closed.

If a User IO program is being used for either Learn or
Recall/Test, NWorks calls the User function UIO_Rewind( ).

:i/o@attentionio
              --- Help about I/O / Attention IO ---

Sometimes it is necessary to start an interaction with a User IO
program which is not related to the input or output of data.
Attention IO allows you to do that by calling the User function
UIO_Attention( ).

:instrument@initialize
           --- Help about Instrument/Initialize---

Selecting this command initializes all instruments.  Initialize
individual instruments with the Graph/Initialize tool.
:instrument@selectprobe

           --- Help about Instrument/Select Probe ---

The Select probe menu allows you to create or modify probes.  A 
probe is a means of pointing to groups of processing elements 
and/or connections for the purpose of later extracting values 
from them.  

Probes are comprised of "probe components" which are added to the 
currently selected (highlighted) probe by using the 
Probe tools for Layer, PE, Layer Connection, PE Connection, Fan-in 
and Fan-out, as well as the Active Connection probe tool in the
Graph Palette.  A probe component can have just a source (PE or
Layer) or it can have a source and destination (PE or Layer).  

The command buttons and their functions are:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

:instrument@outline
           --- Help about Instrument/Outline ---

Selecting this command temporarily forces all instruments to be
displayed in outline mode.  This is useful if there are many
instruments and you need to view or select a network layer lying
beneath them.  The original display modes can be recovered
through the Instrument/Revert command.
:instrument@disable
           --- Help about Instrument/Disable---

Selecting this command temporarily disables graphing and logging
for all instruments.  The original activities can be recovered
through the Instrument/Revert command.
:instrument@enable
           --- Help about Instrument/Enable---

This command enables all instruments, regardless of their last
saved state.  That is, if you had edited an instrument and
disabled graphing, this command would still enable it.
:instrument@revert
           --- Help about Instrument/revert---

Selecting this command reverts to the last edited and saved
display modes and activities for all the instruments.

:run@learn
                  --- Help about Run/Learn ---

For         The network repeats the learning process the number 
            of times specified in the data entry field.  When it 
            encounters an end-of-file on File Sequential input, 
            it will rewind the file and start at the beginning.

One Pass/All
            The network learns through as many data items as are 
            in the data file (one complete pass through the 
            file).  For each item the network will read an input 
            training vector and, if the network is hetero-asso-
            ciative, a corresponding desired output response 
            vector.

            If a User IO program is in use, learning will con-
            tinue until the User IO program terminates learning.

One         The network learns a single item. 

Now         NWorks assumes that the input layer has the desired 
            inputs and the output layer has the desired outputs, 
            and it then performs a single learning cycle.

Until       Learning proceeds until the learn counter reaches 
            the value in the entry box.  As with Learn For, the 
            learning file is automatically rewound when the end 
            of the file is reached.

:run@test
                   --- Help about Run/Test ---

Test lets you test how well your network has learned.  While no 
learning is done, desired output is expected and should be 
present in the Test/Recall file (or provided by the User IO 
program).  

There are two ways to view the results of a test:

  * Look at the .nnr results file which is automatically written
    to during any recall or test. This file has the same root 
    name as the source. There is an option, selectable in the 
    IO/Parameters command dialog box, to print out desired 
    output in this file as well as actual output. 

  * Use NeuralProbe to graph the results of the recall.

Test options are:

For         Causes the recall cycle to repeat for a number of 
            times specified in the For field

One Pass/All
            The recall cycle repeats through one complete pass 
            through the data file

One         A single recall cycle.

:run@recall
                  --- Help about Run/Recall ---

Use Recall to process data through a properly trained network.  
Recall does no learning, and expects no desired outputs.

For         NWorks repeats the recall process the number of 
            times specified in the data entry field. Input 
            begins at the current record. If the input file is 
            exhausted, it will be rewound. To ensure that you 
            start the Recall For from the beginning of the file, 
            do a Rewind Input first.

One Pass/All
            Repeats the recall cycle continuously through one 
            pass of the data file or until an input error oc-
            curs. The input file is rewound prior to the start 
            of One Pass/All.

One         Performs a single complete recall, including reading 
            the next set of input data and writing out the 
            results. For networks such as Hopfield, BAM, and 
            brain-state-in-a-box (which have iterative recalls), 
            the number of iteration steps (Run/Recall Step 
            command) is determined by the maximum count speci-
            fied in the Recall portion of the Learning and 
            Recall Schedule.

Now         Similar to One, but assumes that the inputs have 
            already been applied.
:run@recallstart
               --- Help about Run/Recall Start---

Resets the recall counter to 1 and performs other initialization
procedures including reading the next input record.  This is
usefule for analyzing networks with iterative recalls.  Refer
to Run/Recall Step for more details.
:run@recallstep
                --- Help about Run/Recall Step---

Once the network has been initialized through Run/Recall Start,
this performs one step through it.  For feedforward networks
such as adaline, perceptron, back propagation, and counter
propagation, the recall is completed in a single step.  For
networks like Hopfield, bi-directional associative memory (BAM),
Brain-State-in-a-Box, and Recirculation, this will cause a single
iteration to occur. Recall Step should be repeated until the
current recall portion of the Learning and Recall Schedule is
exhausted.  This allows you to watch the convergence process.
:run@flashcode
                --- Help about Run/Flash Code ---

Using Flash Code, you can almost instantly convert your trained
feed-forward network into a C code callable function.  You can
also select the file type for the target computer platform,
choose single or double precision and set a value as the effective
zero.

Back-propagation networks are the most common type of 
feed-forward networks.

To use Flash Code:

1.    Create and fully train your network.

2.    Select the Flash Code command from the Run menu.

3.    Enter the name of the file for your routine in the 
      File Name field.

4.    Select a radio button to create a Macintosh, PC or 
      Unix file type.

5.    Enter the name of the routine in the Routine Name field.

6.    Select a radio button for single or double precision.

7.    Enter an Effective Zero value in the text field.

8.    Click OK.

Key points:

If you find RRand or GRand as called externals in your Flash 
Code routine, compile it with n2rand.c.  n2rand.c includes 
n2rand.h.

Designer Pack routines and Flash Code routines are completely 
interchangeable.  The only difference is that weight values in Flash 
Code routines are hard coded; i.e., Flash Code routines cannot be 
trained.

:run@explain
                --- Help about Run/Explain---

In some network applications, you may find that you need to know
which of the inputs has the most effect on the output.  The
Explain command provides a quick and easy mechanism for discerning
this.  Explain allows you to dither or jog the input values
(from Recall) by plus or minus a percentage of the Input Network
Range values set in the IO/Parameters command dialog box, and then
view how much the outputs changed as a percentage of the input
change.  This information can either be written to the results
file (.nnr file), or can viewed through a dialog box.

To use Explain:

1.    Select the Parameters... command from the IO menu.

2.    Set the Input Network Ranges field values appropriately. 


3.    Select the Explain command from the Run Menu.

4.    Select the percentage of dither in the Dither text field.

5.    Select either For, One Pass/All, One or Now.  "For" dithers
      the input for the number of data items in the recall file 
      selected in the For text field.  "One Pass/All"  dithers the
      inputs for one complete pass through the Recall file.  "One"
      simply dithers the inputs for a single item in the recall
      file.

The Now radio button produces a dialog box that shows you the 
results of dithering the data currently in the input buffer.

Repeating the Explain Now will simply produce the same results, as 
the Now function does not increment to the next Recall data item.  
:run@explainnow
This shows the result of dithering the current input vector. Each
row represents an Input Processing Element whose label and value
are shown in the first two columns.  The remaining columns show
the signed percentage changes in the outputs when the inputs
are dithered by the requested percentage of the input range.

The percentage and sign of the output results are relative to
the input change.
                   --- Help about Run/Explain---

:run@changecounters
             --- Help about Run/Change Counters ---

Use this dialog box to change counters which affect network 
activity.  

Learn       Selects the "learning" parameters from the L/R 
            Schedule.  This counter is altered by network execu-
            tion.  

Recall      Selects the "recall" parameters from the L/R Sched-
            ule. This counter is altered by network execution.

Layer       Holds the number of the current layer being proc-
            essed.  Layers are numbered starting with zero for 
            the input layer, and incrementing with each higher 
            level on the display.  This is the value passed to 
            the User IO program as the layer number.


Aux 1, Aux 2 and Aux 3 
            These counters are network dependent.  For example, 
            Aux1 is used to specify the accumulation period for 
            Cumulative Back-Propagation.  Aux 1 is also used to 
            specify the length of an epoch for instruments.


:run@checkpoints
               --- Help about Run/Check Points ---

Use the Check Points to periodically do the following during
Learning or Recall/Test:

  * Update the display
  * Dump the screen to a TIF (Tagged Image Format) File.
  * Save the network periodically during the learning or recall 
     process  

These checkpoints are only valid if the IO source is File mode.  
You also have access to the Pruning facility, Weight Limiting
facility, and DNNA discretization which are valid regardless of
the IO source.  

This dialog box is divided into two parts: the top part is for 
learning and the bottom part is for recall.

A learn check point will cause the desired actions to occur every
time the indicated number of training cycles is completed.
(Actually each time the "Learn" counter modulo the Learn check
point count is zero.)

A recall check point is triggered every time the indicated number 
of recall cycles is completed.  (Actually, each time the "Recall" 
count modulo the Recall check point count is zero.)  To get the 
display to update every recall, set the Recall check point count 
to 1, and check the "Re-Display" box below.

When saving the network, the mode used will be the one from the 
File/Save Window command dialog box.  The file name will also be 
taken from that menu.  If the file name ends in one or more 
digits, the name will be incremented prior to each save operation.

The Pruning facility allows you to disable weights in a network.  
The theory is that a minimally complex network that does well on 
a training set will generalize better than a more complex net-
work.  Select the Prune button and choose a percentage value 
between 0 and 100.

Whenever a checkpoint is reached, the following weights will be 
disabled, based on the additional following choices:

Max:        All the weights with an absolute magnitude below the 
            specified percentage of the maximum absolute magnitude
            will be disabled.

Ave:        All the weights with an absolute magnitude below the 
            specified percentage of the average absolute magnitude
            will be disabled.

Med:        All the weights with an absolute magnitude below the 
            specified percentage of the median absolute magnitude
            are disabled.

Pct:        The specified percentage of weights of the smallest 
            absolute magnitude are disabled.

See the "Run Menu" chapter, "Check Points" section of the Reference 
Guide for additional information about Stochastic Pruning, which 
uses Learning Coefficient 3.

The Weight Limiting facility allows the user, by checking the
weight button and specifying a maximum absolute weight limit,
to bring all network weights within limits during every active
checkpoint.  Weight signs will be unchanged.  Note that Pruning
will take place before Weight Limiting.

The DNNA check box is used to discretize DNNA network weights
after learning.  Typically, after some period of learning this
box is selected with the Learn check point count set to 1, and
the network is trained for one or two more data presentations.
This check box must be deselected before further training takes
place.

:arrow
               --- Help about the Pointer Tool ---

Use the pointer tool to:

  * Edit objects within a network
  * Move (or resize) objects within a network  
  * Select commands from the menus
  * Deselect ("drop") other tools

The following covers editing or moving objects:


To Select and Edit a Processing Element (PE)


  1.Double click on a PE.

The PE information is loaded into the PE/Edit tool dialog box. 
[See the "Processing Element (PE) Palette" chapter in the Reference 
guide.]


To Edit IO Parameters for network

  1.Double click away from any PEs.

This short cut has the same effect as selecting the Parameters 
command from the IO/Menu.  (See the "IO Menu" chapter in the 
Reference Guide.)


To Move a Layer 

  1.Click and hold the mouse button over any PE in that layer.

  2.Drag marquee into position.

  3.Release mouse button.

(See the "NWorks Procedures" chapter in the Reference Guide.)


To Move a Network

  1.Click and hold the mouse button away from any PEs.

  2.Drag marquee into position.

  3.Release mouse button.

(See the "NWorks Procedures" chapter in the Reference Guide.)


To Move an Instrument

  1.Click and hold the mouse button over the instrument graph.

  2.Drag the instrument outline box into position.

  3.Release the mouse button.

(See the "NWorks Procedures" chapter in the Reference Guide.)


To Resize an Instrument

  1.Click and hold just within any corner of the instrument 
     graph (mouse pointer becomes "corner").

  2.Drag the outline box corner to shrink or expand the graph.

(See the "NWorks Procedures" chapter in the Reference Guide.)
:layer@add
                --- Help about Layer/Add Tool ---

Use this tool to add a new layer to the network.  The layer will 
be added at the point you clicked.

The lowest layer on the screen is the input layer.  You cannot make 
connections to the input layer, though you CAN make connections from 
it.  The highest layer on the screen is the output layer.  Layers are
always processed from the input to the output.


When you add a layer, you can specify all of the parameters 
required. 

The fields which you can change are:

Name        A name that you can give to the layer to identify 
            it.

# of PEs    The number of processing elements you want in this 
            layer.  

# of rows   For most applications, leave this at 1.  If your 
            application has a layer that corresponds to a two 
            dimensional grid of values, use this parameter to 
            display the layer as a set of rows corresponding to 
            the grid.

# of wgt fields
            Note:  Once a layer has been created, you cannot
            adjust the number of weight fields per connection
            for that layer.

            Most learning rules use a default of two weight fields
            per connection.  However, for some learning rules, a
            single weight field is sufficient and the second weight
            field is only used if you want to monitor delta weights.
            In these cases you can conserve memory by selecting one
            weight field per connection.
            
            The delta-rule uses the second weight field for
            momentum; if you specify only one weight field for the
            delta-rule, you will not be able to use momentum.
            
            The cumulative and normalized cumulative delta rules
            require a third weight field if you wish to use
            momentum.  Since the default value in the "# wgt fields"
            text field is 2, you must explicitly set it to 3 if you
            want to use momentum.  This is usually the only
            situation in which you will need to set this field to
            a value greater than 2.
            
            Some learning rules require many more weight fields per
            connection.  For example DBD and EDBD require six and
            seven weight fields respectively.  However, if the
            "# wgt fields" is set to a number that is too small,
            NWorks will automatically adjust the value to the
            minimum number of weight fields required.

Spacing     The "distance" on the display between each process-
            ing element.

Shape       The shape to be used for processing elements in this 
            layer. Check the box of the shape desired: Square, 
            Triangle or Circle. 

Summation   The method used for computing the weighted sum. 

Transfer    The function applied to the weighted sum.  

Output      Competition for output or learning.  "Direct" means 
            none.  

Error       The function applied to the error.  Quadratic 
            squares the error, and Cubic cubes the error, there-
            by increasing the importance of large errors.  

            Error function tables referenced by the Load Error 
            Function command also appear in this scroll box.

Learning Rule
            The learning rule to be applied to this processing 
            element.  

L/R Schedule
            The learning and recall schedule to apply to this 
            layer.  

Scale and Offset
            The coefficients of a scaling applied to the output.  
            This is a linear transform of the form sX+o. 

Low and High Limits
            Clip the result of scaling and offset to these 
            values.  

F' Offset   For the sigmoid and hyperbolic tangent transfer 
            functions, a constant offset is added to the deriva-
            tive of the transfer function.

            For example: in backprop, as a PE saturates, the 
            derivative of the transfer function approaches zero.  
            Since the derivative of the transfer function is 
            used in the weight update, a zero value would mean 
            no learning could take place.  Adding a small con-
            stant to the derivative would allow some learning to 
            take place.  

Init Low and Init High
            The layer specific randomization limits used in 
            Network/Initialize.
:layer@edit
             --- Help about the Layer/Edit tool ---

Use this tool to change parameters in an existing layer.  Select 
the layer to edit by clicking on it.  The parameters are the same 
as for the Layer/Add tool, except that you cannot change the 
number of processing elements in the layer or the number of 
weight fields.  Use the PE/Add or PE/Delete tools to change the 
number of PEs in a layer.

Name        A name that you can give to the layer to identify 
            it.

# of PEs    Can't change (can only be set when you create the 
            layer with the Layer/Add or Layer/Clone tool).  

# of rows   For most applications, leave this at 1.  If your 
            application has a layer that corresponds to a two 
            dimensional grid of values, use this parameter to 
            display the layer as a set of rows corresponding to 
            the grid.

# of wgt fields
            Can't change (can only be set when you create the 
            layer with the Layer/Add or Layer/Clone tool).

Spacing     The "distance" on the display between each process-
            ing element.

Shape       The shape to be used for processing elements in this 
            layer. Check the box of the shape desired: Square, 
            Triangle or Circle. 

Summation   The method used for computing the weighted sum. 

Transfer    The function applied to the weighted sum.  

Output      Competition for output or learning.  "Direct" means 
            none.  

Error       The function applied to the error.  Quadratic 
            squares the error, and Cubic cubes the error, there-
            by increasing the importance of large errors.  

            Error function tables referenced by the Load Error 
            Function Table command also appear in this scroll 
            box.

Learning Rule
            The learning rule to be applied to this processing 
            element.  

L/R Schedule
            The learning and recall schedule to apply to this 
            layer.  

Scale and Offset
            The coefficients of a scaling applied to the output.  
            This is a linear transform of the form sX+o. 

Low and High Limits
            Clip the result of scaling and offset to these 
            values.  

F' Offset   For the sigmoid and hyperbolic tangent transfer 
            functions, a constant offset is added to the deriva-
            tive of the transfer function.

            For example: in backprop, as a PE saturates, the 
            derivative of the transfer function approaches zero.  
            Since the derivative of the transfer function is 
            used in the weight update, a zero value would mean 
            no learning could take place.  Adding a small con-
            stant to the derivative would allow some learning to 
            take place.  

Init Low and Init High
            The layer specific randomization limits used in 
            Network/Initialize.

:layer@clone
             --- Help about the Layer/Clone tool ---

Use the layer clone to create additional layers that are the same 
as, or similar to, an existing layer.  The Layer/Clone tool 
dialog box has the same capabilities as the Layer/Add dialog box, 
and will be preloaded with the values from the Reference Layer.  
You can change this as desired.

:layer@delete
            --- Help about the Layer/Delete tool ---

Delete a layer.  Click on the layer you want to delete.  You will 
be asked to confirm your decision to delete a layer.  Once deleted,
there is NO "undo" command.

:layer@probe
             --- Help about the Layer/Probe tool ---

Use this tool to select any existing probe, or create a new probe 
at this point.  When you click on Add Component, the layer you've 
selected (with the mouse) is added to the list of components for 
the currently selected probe.  If you try to add the component to 
the default probe, NWorks will create a new probe (called "unti-
tled") to hold this component.  It will not allow you to add 
components to the default probe.

If you use the NEW button to create a new probe without specify-
ing a new name in the Name field, the new probe will automatical-
ly take the name "untitled".  Of course, you can change its name to 
anything you like through the RENAME button.  If you again create 
a new probe without specifying a name, this additional new probe 
will take the name "untitle0", and each unnamed new probe after 
this will increment the number at the end of "untitle".

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.

:layer@initialize
          --- Help about the Layer/Initialize tool ---

Use this tool to randomize incoming variable weights to the layer
you click on.  Fixed, set and modified weights are not affected.  
The weights are randomized either Uniformly (between the Init
Low and Init High limits specified in the Layer Add, Edit or Clone
tool dialog box) or using a Gaussian distribution, in which case
the limits represent +/- 1 sigma. The type of distribution is
determined by the layer's noise function.  If the layer has a
Kohonen, Kohonen1, KohonenN or SPR learning rule, the weight
vector will be multiplicatively post normalized.  Also, calls are
made to user-defined math function initialization routines if
these exist as part of the User-Defined Neuro-Dynamics for the
selected layer.

Initializing will zero all PEs in the layer and the delta weights 
associated with each connection.
:layer@randomizeweights
           --- Help about Layer/Randomize Weights ---

Use this tool to randomize all of the variable weights in the 
layer.  Fixed, set and modified weights are not affected.  The
weights are randomized either Uniformly between the "Low Limit"
and "High Limit" or using a Gaussian distribution, in which case
the limits represent +/- 1 sigma.   The type of distribution is
determined by the layer's noise function.  If the layer has a
Kohonen, Kohonen1, KohonenN or SPR learning rule, the weight
vector will be multiplicatively post normalized. 

When the weights are randomized, all Delta Weights and auxiliary
weight are set to zero.  Disabled weights are re-enabled, and the
learn counter is set to zero.

To Use the Tool

Simply click on the layer you want to randomize.

:layer@jogweights
          --- Help about the Layer/Jog Weights tool ---

Use this tool to jog (add a random number) to all of the variable 
weights in the layer.  "Low Limit" and "High Limit" have the same
meanings as for the Randomize Weights tool.

When weights are jogged, the Delta Weights and Auxiliary weights
are set to zero.

Jogging weights for a processing element which uses the Kohonen, 
Kohonen1, KohonenN, or SPR learning rule will cause the weights to be 
multiplicatively post-normalized as appropriate.

To use this tool, simply click on the layer.
:layer@zerodeltaweights
      --- Help about the Layer/Zero Delta Weights tool ---

Use this tool to zero the Delta Weights and Auxiliary weights
of all incoming variable connections to a layer.

:layer@showconnections
         --- Help about Layer/Show Connections ---

Use this tool to display previously hidden connections.  Click 
the radio button to choose to redisplay:

  * Input Connections
  * Output Connections
  * All Connections (both input and output)

For densely connected layers, the resulting screen update time 
can be quite extensive.  It is often helpful to Hide all connec-
tions (Network/Hide Connections command) and then selectively 
redisplay them on a processing element or layer basis.  

This command affects only the Network style of display.


:layer@hideconnections
         --- Help about Layer/Hide Connections ---

Use this tool to hide connections to and/or from the layer you 
click on.  Click the radio button to choose to hide:

  * Input Connections
  * Output Connections
  * All Connections (both input and output)

Connections can be selectively redisplayed by using any of the 
Show Connections tools from the object palette, or through the 
Network Show Connections command.  

This command affects only the Network style of display.
:layer@l/rschedule
        --- Help about the Layer / L/R Schedule tool ---

Use this tool to edit, load, save or create a learning and recall 
schedule for a particular layer. The rows and columns in the 
schedule are described in the "Learning and Recall Schedules" 
chapter in the Reference Guide.

The L/R schedule displayed is the one which applies to the layer 
you've selected. If the layer uses the Network Global schedule, 
that will be displayed.  When you click OK to exit from the menu, 
the currently edited schedule will be applied to the layer. 


Edit a Schedule in Memory

  1.Select a schedule from those in the directory listing 
     above In Memory.  

It will appear in reverse video to show that it is selected. 

  2.Click the box labeled EDIT. 

  3.The box will display in reverse video until you release the 
     mouse button. The dialog box will be refreshed showing the 
     parameters for the selected schedule. The name to the left 
     of Name (lower center of the display) is the name of the 
     currently edited schedule.

The highlighted schedule in the In Memory scroll window only 
becomes the edited schedule when the above procedure is followed; 
there can be a disparity between the highlighted name and the 
name of the schedule actually being edited. Also, the name to the 
left of Name may differ from the name of the currently edited 
schedule. The currently edited schedule is renamed to the name in 
the entry box whenever any action button in the dialog box is 
pressed.

Clear a Schedule from Memory

  1.Select the schedule from the In Memory window. 

  2.Click the CLEAR button.

The schedule will be removed from memory.


Create a New Schedule in Memory

  1.Click the button labeled NEW. 

A new schedule named "untitled" will be created. If you click the 
button again, a new schedule named "untitle0" will be created, 
and so on.  You can change the name to something more appropriate 
by changing its name in the Name field. 

The previously edited schedule is used as a prototype for 
the new one. 


On Disk 

Just above On Disk is a directory listing of the schedules in the 
current directory on disk. There are two functions which you can
perform: 

Load a schedule from disk, or save a schedule to disk.


Load a Learn/Recall Schedule From Disk

  1.Select the schedule you wish to load in the directory 
     window immediately above On Disk. 

  2.Click the LOAD button. 

The selected schedule will be loaded into memory, added to the In 
Memory directory, and displayed as the currently edited schedule. 
When a schedule of the same name as one already resident in 
memory is loaded, it will overwrite the contents of the one in 
memory. You will NOT be asked to confirm that it is OK to over-
write it.


Save a schedule

  1.Click the button labeled SAVE.

The currently displayed schedule will be saved. The file name 
will be the same as the name displayed to the left of Name, 
except that it will have a .nnt extension.  It will be saved as a 
type TEXT file.


Changing the Name of a Schedule

You can change the name by editing the box to the left of the 
Name in the lower center of the display. When the name is 
changed, it will be changed in the In Memory directory the next 
time you enter this menu, or when you edit, load, create or save 
a schedule.

:pe@add
               --- Help about the PE/Add tool ---

Add a processing element to a layer.  The processing element will be 
added to the layer closest to the cursor.  If the cursor is to the 
right of center of a processing element, the new processing element 
will be added to the right.  Otherwise to the left.  There is no 
dialog box associated with this choice.

You can use the PE Connection/Add tool (or the Fan-out/Add or 
Fan-in/Add tool) to establish connections for this new processing 
element.  You can also use the PE Connection/Clone tool to copy 
the connections from another element in the layer to your new PE.


:pe@edit
               --- Help about the PE/Edit tool ---

Use this tool to edit the internals of a processing element and 
examine:

  * All of its connections
  * Current connection input levels
  * Connection weights
  * Change in weight  


You can also:

  * Change the name of the processing element.  
  * Enable or Disable the PE.

The text fields are:

Name        Every processing element has a name.  If none is 
            assigned by the user, the processing element number 
            is used.

Sum         The result of the summation function.  To manually 
            set the input of a network, change this value.

Tran        The result of applying the transfer function to the 
            weighted sum.

Output      The current output of this processing element.  This 
            is the same as "Tran" unless there is competition.

Desired     This shows the desired output for heterogeneous
            networks in learn or test mode.

Error       Depending on the control strategy, during a learn, 
            this will be 1) the desired output; 2) the differ-
            ence between the desired and actual output; 3) The 
            current back-propagated error scaled by the deriva-
            tive of the feed-forward transfer function.  If a 
            Run/Test was done, this field contains the desired 
            output.

Cur Err     The current error. In networks where an error is 
            calculated, this field shows the difference between 
            the desired and actual output.  It is sometimes 
            referred to as the raw error.  If a Run/Test, was 
            done, then this field can be graphed with instrumen-
            tation to provide a measure of network performance.  

Err Fac     The error factor.  This field is a scales that can 
            be used to increase or decrease the error associated 
            with a particular PE.  It is usually used in the 
            output layer.

The columns used in the connection display are:

PE          The name (or number) of the processing element where 
            the connection originated.

Input       The current output of the source PE (the input to 
            this connection).

Weight      The value of the connecting weight between the 
            source PE and this one.

Type        The mode of weight (V=Variable, F=Fixed, S=Set, 
            M=Modified, D=Disabled) and the connection scope 
            (a=Absolute, r=Relative, l=Local).

Delta Wt    The last weight change or accumulated weight change 
            (Cum Delta Rule).

:pe@clone
              --- Help about the PE/Clone tool ---

Use the Clone Tool to make a copy of a PE.  The clone will be an 
exact copy of the selected PE, and a dialog box lets you also
clone the input, output or all connections.  After cloning a PE,
you should initialize it with the PE/Initialize tool.

The location you pick for the cloned PE must be in the vicinity 
of a layer, otherwise NWorks will continue to prompt you for a 
location.  To establish a new layer (even if it has only one PE), 
use the Layer/Add tool. 

If the location you pick for the cloned PE is between two exist-
ing PEs, the new PE will be added between them. If it is to the 
right of the last PE in the layer, it will be added to the end of 
the layer. If it is to the left of the first PE in the layer, it 
will be added to the beginning of the layer.

Connection scope also affects cloning.  Keep the following rules 
in mind:

  * Absolute connections are copied, and have the same connecting
    PEs in both the reference PE and the clone.

  * Relative connections are copied, but the relative geometry 
    of the connections is copied from the reference to the clone 
    PE.  Refer to the "PE Palette" chapter, "Clone Tool" section 
    in the Reference Guide for a detailed explanation and
    illustration of how this works.

  * Local connections aren't copied in the clone PE.
:pe@delete
              --- Help about the PE/Delete tool ---

Click on a PE to delete it.  When the processing element is 
deleted, the display will be re-drawn, removing it from its place 
in the layer.

CAUTION: make sure you reselect the pointer tool after you have
finished with the PE/Delete tool.  This will help you avoid
accidentally deleting additional PEs.
:pe@probe
              --- Help about the PE/Probe tool ---

Use this tool to select any existing probe or create a new probe.  
When you click on Add Component, the PE you've selected (with the 
mouse) is added to the list of components for the currently 
selected probe.  If you try to add the component to the default 
probe, NWorks will create a new probe (called "untitled") to hold 
this component.  It will not allow you to add components to the 
default probe.

If you use the NEW button to create a new probe without specify-
ing a new name in the Name field, the new probe will automatical-
ly take the name "untitled".  Of course, you can change its name to 
anything you like through the RENAME button.  If you again create 
a new probe without specifying a name, this additional new probe 
will take the name "untitle0", and each unnamed new probe after 
this will increment the number at the end of "untitle".

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.
:pe@initialize
            --- Help about the PE/Initialize tool ---

Use the Initialize tool to randomize the variable weights
associated with a PE.  Refer to the layer initialize tool for
details of the randomization.  Unlike the layer initialize tool
you will be presented with a dialog giving you the option
to initialize incoming weights, outgoing weights or both.

Initializing will zero the delta weights and auxiliary weights
associated with each connection.

:pe@randomizeweights
        --- Help about the PE/Randomize Weights tool ---
                                
Use this tool to randomize all of the incoming variable weights to
a PE.  Refer to the layer randomize tool for details of the
randomization and how it differs from the Initialize tool.

:pe@jogweights
           --- Help about the PE/Jog Weights tool ---

Use this tool to Jog (add a random number) to all of the incoming
variable weights to a processing element.  Refer to the
corresponding tool for layers for more details.

:pe@zerodeltaweights
        --- Help about the PE/Zero Delta Weights Tool ---

Sets to zero the delta and auxiliary weight fields for the
incoming variable weight connections to a PE.
:pe@showconnections
         --- Help about the PE/Show Connections tool ---

Use this tool to display previously hidden connections.  Click 
the radio button to choose to redisplay:

  * Input Connections
  * Output Connections
  * All Connections (both input and output)

For densely connected layers, the resulting screen update time 
can be quite extensive.  It is often helpful to Hide all connec-
tions (Network/Hide Connections command) and then selectively 
redisplay them on a processing element or layer basis.  

This command affects only the Network style of display.


:pe@hideconnections
         --- Help about the PE/Hide Connections tool ---

Use this tool to hide connections to and/or from the PE you click 
on.  Click the radio button to choose to hide:

  * Input Connections
  * Output Connections
  * All Connections (both input and output)

Connections can be selectively redisplayed by using any of the 
Show Connections tools from the object palette, or through the 
Network Show Connections command.  

This command affects only the Network style of display.

:graph@easyprobe
         --- Help about Graph/Easy Probe ---

EasyProbe provides the primary (and easiest) way to build probes and 
instruments to monitor your network.  Creating a probe/instrument 
combination is a simple matter of selecting:

The variable to be graphed.  This defines the type of data 
that the instrument will monitor.

The type of probe.  Probes provide input to the instrument, 
and are attached to processing elements, connections or both.

The type of graph.  This defines the way that the data is 
graphically presented.

You can also select to have the data transformed across an Epoch, 
should one be in use.  Normally, the data from a probe is combined 
across probe points.  If you check this box, the data will also be 
transformed across training cycles; in the case of histograms and 
confusion matrices, data is accumulated across an epoch.

EasyProbe is an intelligent instrument facility, in that 
it provides only those graph types that are useful with the
combination of the variable graphed and the probe type that
you've chosen.  It examines functions within the network and
provides reasonable default scales for the various instruments
you create (although you may wish to modify these scales to more
appropriate values for some applications).

The following sections detail each of the options in EasyProbe.

Variable Graphed

Current Error:       This is the current or raw error value for 
                     each of the PEs within the probe.

RMS Error:           The Root Mean Square of the errors across the
                     PEs specified by the probe type.

Output Value:        If there is no competitive output, the output
                     value is equal to the output of the transfer
                     function.

Summation Value:     The last computed weighted sum for the PEs
                     specified in the probe.

Individual Weights:  The individual connection weights for component 
                     specified in the probe.

RMS Weights (PE):    The RMS weight values for each PE specified in
                     the probe.

RMS Weights (Layer): The RMS weight value for the layer specified 
                     by the probe.


Probe Type

Entire Network:      All the PEs or Connections within the entire
                     network are selected as probe points.

Output Layer:        All of the PEs in the output layer are selected
                     as probe points.

Selected Layer:      You will be prompted to select a layer (by
                     clicking on it) as the probe component. 
                     Probe points become all PEs or connections 
                     within the selected layer.

Input Layer:         This selects the input layer as the probe
                     component (with all PEs or connections within
                     the layer selected as probe points).

Selected PE:         You will be prompted to select an individual
                     PE as the probe component.  You select the PE
                     by clicking on it.



Graph Type


The variable and probe type combination provides only those
options within the Graph Type scroll window that provide useful
instruments.  If a variable and graph type combination is not
allowed, no graph type will display in the scroll window.  The
set of graph types available in Easy Probe is:

Strip chart

Bar chart

Histogram

Confusion Matrix

Hinton diagram

To create confusion matrices, you must have the epoch box checked 
and the output value variable selected.  If a layer is displayed 
as a two-dimensional layer, then bar charts and Hinton diagrams will 
appear as two-dimensional arrays for the Output and Summation values.
:graph@add
              --- Help about the Graph/Add tool ---
                                      
Use this tool to Add a new instrument to the network.  The in-
strument will be added at the point you select (by clicking).

When you add an instrument, a dialog box appears which allows you 
to change the parameters defining an instrument.  These parame-
ters are initialized with default values.

The parameters you can change are:

Instrument Title
            A name you can assign the instrument which is used 
            to label graphs.  The name also forms part of the 
            header for the logging option. 

Probe       The name of the probe the instrument will use to 
            extract information.  Select the probe by clicking 
            on its name in the scroll box.

Variable    The specific variable associated with a probe point 
            whose value is to be extracted.  

Trans. Mode How probe points are combined into sets for input to 
            the transformation.  

Trans. Type The transformation used to combine the values from a 
            set of probe points into a single value.  Transfor-
            mations available in the scroll window are: Avg 
            (average), RMS (root mean square), Max (maximum), 
            Min (minimum), MAV (mean absolute value), MxAV 
            (maximum absolute value) and MnAV (minimum absolute 
            value).  

Epoch       Check box used to combine and transform probe points 
            or sets of probe points over an epoch.  An epoch is 
            defined as a certain number of learning cycles which 
            corresponds to the Epoch Size value in the IO/Param-
            eters dialog box or the Aux 1 value in the 
            Run/Change Counters dialog box.  This option can be 
            chosen whether or not the network uses epoch (cumu-
            lative) learning.

Learn       This check box enables or disables the instrument 
            for learn functions. 

Recall      This check box enables or disables the instrument 
            for recall functions.
 
Learn Div   (Divisor) Used in conjunction with the learn remain-
            der parameter to control on which learn cycles 
            instrument data is extracted and processed.  

Learn Rem   (Remainder) Used in conjunction with the Learn Div 
            parameter to control on which learn cycles instru-
            ment data is extracted and processed.  This occurs 
            on cycles where the remainder on dividing the learn 
            counter  by the learn divisor is equal to the learn 
            remainder.  A learn divisor of 0 is equivalent to a 
            learn divisor of 1.  If the epoch option is select-
            ed, the learn divisor and remainder take the role of 
            epoch divisor and remainder.  

Recall Div  (Divisor) Used in conjunction with the recall re-
            mainder parameter to control at which steps in a 
            recall instrument data is extracted and processed.  

Recall Rem  (Remainder) Used in conjunction with the recall 
            divisor parameter to control at which steps in a 
            recall instrument data is extracted and processed.  
            This occurs at the end of steps where the remainder 
            on dividing the recall counter by the recall divisor 
            is equal to the recall remainder.  A recall divisor 
            of 0 together with a non zero recall remainder 
            indicates that the instrument is updated only when 
            the recall count is equal to the recall remainder.  
            If both recall divisor and recall remainder are 
            equal to zero, The instrument is updated at the end 
            of a recall cycle.

Graphing Active
            Graphing is active for this instrument only if this 
            box is checked.

Graph Type: Click on the type of graph: strip chart, bar graph, 
            matrix plot, confusion matrix, or histogram.  

vmin and vmax 
            (Value Range) Vmin and vmax represent the low and 
            high limits depicted on any type of graph.  

# x         The number of "items" in the x direction for a 
            graph. "items" means different things for different 
            types of graph. For strip charts, the number of 
            items refers to the number of points plotted along 
            the x axis.  (If, during the plotting, the 
            graph reaches the end of the axis, it automatically 
            recycles to the start of the axis.  A marker indi-
            cates the current point being plotted).  For bar 
            graphs, "# x" refers to the number of bars in one 
            horizontal line of the graph; a bar graph can be 
            displayed as a matrix of bars in order to display 2-
            dimensional data such as character patterns.  For 
            matrix plots, "# x" refers to the number of pixels 
            (picture elements) in the x direction.  For histo-
            grams, "# x" refers to the number of bins.  For a 
            confusion matrix, "# x" represents the number of 
            bins along the x and y axis.  

# plots     The number of plots. For strip charts this refers to 
            the number of variables plotted on the chart.  For 
            bar graphs this refers to the total number of bars.  
            For matrix plots, "# plots" refers to the total 
            number of pixels (picture elements).  For histo-
            grams, "# plots" has no meaning.  
            For a confusion matrix, "# plots" is the total 
            number of bins in the main portion of the matrix.  

Height      Height, in screen pixels, of the graph box.  
            
Width       Width, in screen pixels, of the graph box.  

Plot Colors Up to four plot colors can be defined.  For strip 
            charts these colors are assigned mod 4 to the plots.  
            For bar graphs and histograms, only the first two 
            are used and are assigned mod 2 to the bars and bins 
            respectively.  For matrix plots the plot colors 
            together with the background color represent an 
            partition of the vmin to vmax value range into 5 
            equal value ranges.  

Frgrnd      Color used for the outline of the graph box, and, 
            for strip charts, for displaying the title and 
            position marker.  

Bkgrnd      Background color for graph boxes.  

Background Transparent
            This check box only applies to strip charts and is 
            useful if you want to overlay strip charts.  If this 
            box is checked, no background is painted.  

Disp. Mode  These radio button are provided to display instru-
            ments in outline form. Outline mode allows you to 
            see and select what lies below the instrument.  
            During execution of the network the instrument is 
            displayed in full.

Logging Active
            Logging is active for this instrument only if this 
            box is checked.  

User IO and File
            These radio buttons select whether to direct the 
            values to file or to a user program.  

File Name   The file to which data is logged.  A ".nnp" exten-
            sion is automatically affixed to the root name you 
            provide.  

Write and Append
            These radio buttons select whether data should 
            overwrite or be appended to the logging file.

Convergence Criterion
            Convergence criterion is active for this instrument 
            only if this box is checked.  

Threshold   The value generated by the probe and transformation 
            mechanisms is compared with this threshold.  If the 
            value is less than the threshold, learning is termi-
            nated.

Command Buttons 

UPDATE      There are many parameters which can be set independ-
            ently in the dialog box.  Sometimes these will 
            conflict.  When you exit the dialog box the system 
            will perform various consistency and legality checks 
            and may modify values if necessary.  The UPDATE 
            button allows you to force this while still in the 
            dialog box and to view the results.  

INITIALIZE  This button allows you to initialize (zero out) the 
            data arrays associated with the instrument.


:graph@edit
           --- Help about the Instrument/Edit tool ---

Use this tool to edit an existing instrument.  Simply click on 
the instrument you wish to edit.

When you edit an instrument, a dialog box appears which allows 
you to change the parameters defining an instrument.

The parameters you can change are:

Instrument Title
            A name you can assign the instrument which is used 
            to label graphs.  The name also forms part of the 
            header for the logging option.  Create the name by 

Probe       The name of the probe the instrument will use to 
            extract information.  Select the probe by clicking 
            on its name in the scroll box.

Variable    The specific variable associated with a probe point 
            whose value is to be extracted.  

Trans. Mode How probe points are combined into sets for input to 
            the transformation.  

Trans. Type The transformation used to combine the values from a 
            set of probe points into a single value.  

Epoch       Check box used to combine and transform probe points 
            or sets of probe points over an epoch.  An epoch is 
            defined as a certain number of learning cycles which 
            corresponds to the Epoch Size value in the IO/Param-
            eters dialog box or the Aux 1 value in the 
            Run/Change Counters dialog box.  This option can be 
            chosen whether or not the network uses epoch (cumu-
            lative) learning.

Learn       This check box enables or disables the instrument 
            for learn functions. 

Recall      This check box enables or disables the instrument 
            for recall functions.
 
Learn Div   (Divisor) Used in conjunction with the learn remain-
            der parameter to control on which learn cycles 
            instrument data is extracted and processed.  

Learn Rem   (Remainder) Used in conjunction with the Learn Div 
            parameter to control on which learn cycles instru-
            ment data is extracted and processed.  This occurs 
            on cycles where the remainder on dividing the learn 
            counter  by the learn divisor is equal to the learn 
            remainder.  A learn divisor of 0 is equivalent to a 
            learn divisor of 1.  If the epoch option is select-
            ed, the learn divisor and remainder take the role of 
            epoch divisor and remainder.  

Recall Div  (Divisor) Used in conjunction with the recall re-
            mainder parameter to control at which steps in a 
            recall instrument data is extracted and processed.  

Recall Rem  (Remainder) Used in conjunction with the recall 
            divisor parameter to control at which steps in a 
            recall instrument data is extracted and processed.  
            This occurs at the end of steps where the remainder 
            on dividing the recall counter by the recall divisor 
            is equal to the recall remainder.  A recall divisor 
            of 0 together with a non zero recall remainder 
            indicates that the instrument is updated only when 
            the recall count is equal to the recall remainder.  
            If both recall divisor and recall remainder are 
            equal to zero, The instrument is updated at the end 
            of a recall cycle.

Graphing Active
            Graphing is active for this instrument only if this 
            box is checked.

Graph Type  Click on the type of graph: strip chart, bar graph, 
            matrix plot, confusion matrix, or histogram.  

vmin and vmax 
            (Value Range) Vmin and vmax represent the low and 
            high limits depicted on any type of graph.  

# x         The number of "items" in the x direction for a 
            graph. "items" means different things for different 
            types of graph. For strip charts, the number of 
            items refers to the number of points plotted along 
            the x axis.  (If, during the plotting, the 
            graph reaches the end of the axis, it automatically 
            recycles to the start of the axis.  A marker indi-
            cates the current point being plotted).  For bar 
            graphs, "# x" refers to the number of bars in one 
            horizontal line of the graph; a bar graph can be 
            displayed as a matrix of bars in order to display 2-
            dimensional data such as character patterns.  For 
            matrix plots, "# x" refers to the number of pixels 
            (picture elements) in the x direction.  For histo-
            grams, "# x" refers to the number of bins.  For a 
            confusion matrix, "# x" represents the number of 
            bins along the x and y axis.  

# plots     The number of plots. For strip charts this refers to 
            the number of variables plotted on the chart.  For 
            bar graphs this refers to the total number of bars.  
            For matrix plots, "# plots" refers to the total 
            number of pixels (picture elements).  For histo-
            grams, "# plots" has no meaning.  
            For a confusion matrix, "# plots" is the total 
            number of bins in the main portion of the matrix.  

Height      Height, in screen pixels, of the graph box.  
            
Width       Width, in screen pixels, of the graph box.  

Plot Colors Up to four plot colors can be defined.  For strip 
            charts these colors are assigned mod 4 to the plots.  
            For bar graphs and histograms, only the first two 
            are used and are assigned mod 2 to the bars and bins 
            respectively.  For matrix plots the plot colors 
            together with the background color represent an 
            partition of the vmin to vmax value range into 5 
            equal value ranges.  

Frgrnd      Color used for the outline of the graph box, and, 
            for strip charts, for displaying the title and 
            position marker.  

Bkgrnd      Background color for graph boxes.  

Background Transparent
            This check box only applies to strip charts and is 
            useful if you want to overlay strip charts.  If this 
            box is checked, no background is painted.  

Disp. Mode  These radio button are provided to display instru-
            ments in outline form. Outline mode allows you to 
            see and select what lies below the instrument.  
            During execution of the network the instrument is 
            displayed in full.

Logging Active
            Logging is active for this instrument only if this 
            box is checked.  

User IO and File
            These radio buttons select whether to direct the 
            values to file or to a user program.  

File Name   The file to which data is logged.  A ".nnp" exten-
            sion is automatically affixed to the root name you 
            provide.  

Write and Append
            These radio buttons select whether data should 
            overwrite or be appended to the logging file.

Convergence Criterion
            Convergence criterion is active for this instrument 
            only if this box is checked.  

Threshold   The value generated by the probe and transformation 
            mechanisms is compared with this threshold.  If the 
            value is less than the threshold, learning is termi-
            nated.

Command Buttons 

UPDATE      There are many parameters which can be set independ-
            ently in the dialog box.  Sometimes these will 
            conflict.  When you exit the dialog box the system 
            will perform various consistency and legality checks 
            and may modify values if necessary.  The UPDATE 
            button allows you to force this while still in the 
            dialog box and to view the results.  

INITIALIZE  This button allows you to initialize (zero out) the 
            data arrays associated with the instrument.

:graph@clone
          --- Help about the Instrument/Clone tool ---

Use this tool to make a copy of an existing instrument, or to 
create an new instrument that is similar to an existing instru-
ment.  

When you clone an instrument, a dialog box appears which allows 
you to change the parameters defining the "clone".  The dialog 
box is preloaded with the parameters from the reference instru-
ment.

To clone an instrument:

  1.Click on a reference instrument.

  2.Click on a position to place the clone instrument.

  3.Modify any parameters in the dialog box, then click on OK.

The parameters you can modify are:

Instrument Title
            A name you can assign the instrument which is used 
            to label graphs.  The name also forms part of the 
            header for the logging option.  Create the name by 

Probe       The name of the probe the instrument will use to 
            extract information.  Select the probe by clicking 
            on its name in the scroll box.

Variable    The specific variable associated with a probe point 
            whose value is to be extracted.  

Trans. Mode How probe points are combined into sets for input to 
            the transformation.  

Trans. Type The transformation used to combine the values from a 
            set of probe points into a single value.  

Epoch       Check box used to combine and transform probe points 
            or sets of probe points over an epoch.  An epoch is 
            defined as a certain number of learning cycles which 
            corresponds to the Epoch Size value in the IO/Param-
            eters dialog box or the Aux 1 value in the 
            Run/Change Counters dialog box.  This option can be 
            chosen whether or not the network uses epoch (cumu-
            lative) learning.

Learn       This check box enables or disables the instrument 
            for learn functions. 

Recall      This check box enables or disables the instrument 
            for recall functions.
 
Learn Div   (Divisor) Used in conjunction with the learn remain-
            der parameter to control on which learn cycles 
            instrument data is extracted and processed.  

Learn Rem   (Remainder) Used in conjunction with the Learn Div 
            parameter to control on which learn cycles instru-
            ment data is extracted and processed.  This occurs 
            on cycles where the remainder on dividing the learn 
            counter  by the learn divisor is equal to the learn 
            remainder.  A learn divisor of 0 is equivalent to a 
            learn divisor of 1.  If the epoch option is select-
            ed, the learn divisor and remainder take the role of 
            epoch divisor and remainder.  

Recall Div  (Divisor) Used in conjunction with the recall re-
            mainder parameter to control at which steps in a 
            recall instrument data is extracted and processed.  

Recall Rem  (Remainder) Used in conjunction with the recall 
            divisor parameter to control at which steps in a 
            recall instrument data is extracted and processed.  
            This occurs at the end of steps where the remainder 
            on dividing the recall counter by the recall divisor 
            is equal to the recall remainder.  A recall divisor 
            of 0 together with a non zero recall remainder 
            indicates that the instrument is updated only when 
            the recall count is equal to the recall remainder.  
            If both recall divisor and recall remainder are 
            equal to zero, The instrument is updated at the end 
            of a recall cycle.

Graphing Active
            Graphing is active for this instrument only if this 
            box is checked.

Graph Type: Click on the type of graph: strip chart, bar graph, 
            matrix plot, confusion matrix, or histogram.  

vmin and vmax 
            (Value Range) Vmin and vmax represent the low and 
            high limits depicted on any type of graph.  

# x         The number of "items" in the x direction for a 
            graph. "items" means different things for different 
            types of graph. For strip charts, the number of 
            items refers to the number of points plotted along 
            the x axis.  (If, during the plotting, the 
            graph reaches the end of the axis, it automatically 
            recycles to the start of the axis.  A marker indi-
            cates the current point being plotted).  For bar 
            graphs, "# x" refers to the number of bars in one 
            horizontal line of the graph; a bar graph can be 
            displayed as a matrix of bars in order to display 2-
            dimensional data such as character patterns.  For 
            matrix plots, "# x" refers to the number of pixels 
            (picture elements) in the x direction.  For histo-
            grams, "# x" refers to the number of bins.  For a 
            confusion matrix, "# x" represents the number of 
            bins along the x and y axis.  

# plots     The number of plots. For strip charts this refers to 
            the number of variables plotted on the chart.  For 
            bar graphs this refers to the total number of bars.  
            For matrix plots, "# plots" refers to the total 
            number of pixels (picture elements).  For histo-
            grams, "# plots" has no meaning.  
            For a confusion matrix, "# plots" is the total 
            number of bins in the main portion of the matrix.  

Height      Height, in screen pixels, of the graph box.  
            
Width       Width, in screen pixels, of the graph box.  

Plot Colors Up to four plot colors can be defined.  For strip 
            charts these colors are assigned mod 4 to the plots.  
            For bar graphs and histograms, only the first two 
            are used and are assigned mod 2 to the bars and bins 
            respectively.  For matrix plots the plot colors 
            together with the background color represent an 
            partition of the vmin to vmax value range into 5 
            equal value ranges.  

Frgrnd      Color used for the outline of the graph box, and, 
            for strip charts, for displaying the title and 
            position marker.  

Bkgrnd      Background color for graph boxes.  

Background Transparent
            This check box only applies to strip charts and is 
            useful if you want to overlay strip charts.  If this 
            box is checked, no background is painted.  

Disp. Mode  These radio button are provided to display instru-
            ments in outline form. Outline mode allows you to 
            see and select what lies below the instrument.  
            During execution of the network the instrument is 
            displayed in full.

Logging Active
            Logging is active for this instrument only if this 
            box is checked.  

User IO and File
            These radio buttons select whether to direct the 
            values to file or to a user program.  

File Name   The file to which data is logged.  A ".nnp" exten-
            sion is automatically affixed to the root name you 
            provide.  

Write and Append
            These radio buttons select whether data should 
            overwrite or be appended to the logging file.

Convergence Criterion
            Convergence criterion is active for this instrument 
            only if this box is checked.  

Threshold   The value generated by the probe and transformation 
            mechanisms is compared with this threshold.  If the 
            value is less than the threshold, learning is termi-
            nated.

Command Buttons 

UPDATE      There are many parameters which can be set independ-
            ently in the dialog box.  Sometimes these will 
            conflict.  When you exit the dialog box the system 
            will perform various consistency and legality checks 
            and may modify values if necessary.  The UPDATE 
            button allows you to force this while still in the 
            dialog box and to view the results.  

INITIALIZE  This button allows you to initialize (zero out) the 
            data arrays associated with the instrument.

:graph@delete
          --- Help about the Instrument/Delete tool ---

Use this tool to delete an instrument.  To delete an instrument, 
simply click on it.  You will NOT be asked to confirm your deci-
sion to delete an instrument.  Once deleted, there is NO "undo" 
command.
:graph@instaprobe
             --- Help about the InstaProbe tool ---

InstaProbe is a method for creating a variety of probes and 
instruments by simply selecting the instrument name and the 
values of a small number of important parameters.  InstaProbe 
assists in the creation of instruments in the same way that 
InstaNet helps you create networks.  Any probe and instrument 
created through InstaProbe could be created using the Probe and 
Instrument menu selections; however, using InstaProbe relieves 
the user of having to worry about the large number of options and 
parameter values required in defining probes and instruments.  If 
necessary, a probe and instrument created through InstaProbe can 
be modified through the standard probe and instrument editing 
facilities.  

NOTE:  Many of the instruments that were part of InstaProbe in
earlier releases are now part of Easy Probe.

InstaProbe, Probes, and Instruments are collectively referred to 
as NeuralProbe.

InstaProbe instruments are created from special text "scripts" which 
use a language internal to NWorks; these files have an ".ipb" 
extension.  

The values needed by InstaProbe are:

vmin        Minimum plot value

vmax        Maximum plot value

cthr        Convergence Threshold

# x         Number of items in x direction

"vmin" and "vmax" are used by all instruments and represent the limits 
of the data values depicted on a graph.  For example, if you wanted to 
plot outputs of a standard back propagation network, vmin = 0.0 and 
vmax = 1.0 would be good values to choose since the output of a
sigmoid transfer function lies between 0.0 and 1.0.

"cthr" is only used by instruments which plot RMS (Root Mean Square) 
errors; if it is set to a positive number, then learning is 
automatically terminated when the RMS error falls below cthr.

"# x" has a different meaning for each type of graph supported by 
NeuralProbe:

For strip charts, "# x" refers to the number of points plotted 
along the x axis.  (If, during the plotting, the graph reaches 
the end of the axis, it automatically recycles to the start of 
the axis. A marker indicates the current point being plotted).

For bar graphs, "# x" refers to the number of bars in one hori-
zontal line of the graph; a bar graph can be displayed as a 
matrix of bars in order to display 2-dimensional data such as 
character patterns.

For matrix plots, "# x" refers to the number of pixels (picture 
elements) in the x direction.

For histograms, "# x" refers to the number of bins.

For a confusion matrix, "# x" refers to the number of bins (the 
number of plots is the number of bins squared).

The following instruments are currently supported by InstaProbe:
lay_engy    Energy in a layer shown as a strip chart.

lay_wgtb    Weights between two specified layers plotted as a
            2-D array of bars.  You will be prompted for a source
            and destination layer.

            The dimensions of the 2-D array are determined by 
            the number of processing elements in the source and 
            destination layers.  "#x" and "cthr" are ignored.  
            This instrument is only active during learning.  
            Values are plotted every 10 learning presentations.  
            This display mode assumes the source and destination 
            layers to be fully connected.

lay_wgtm    Weights between two specified layers plotted as a 2-
            D pixel array.  You will be prompted for a source
            and destination layer.
            
            The dimensions of the 2-D array are determined by the
            number of processing elements in the source and
            destination layers.  "#x" and "cthr" are ignored.
            This instrument is only active during learning. Values
            are plotted every 10 learning presentations.  This mode
            of display assumes the source and destination layers
            to be fully connected.

lay_wthn    Same as "lay_wgtm" but displayed as a Hinton diagram.
:graph@activeconnections
    --- Help about the Instrument/Active Connections tool ---

Use this tool to add active connection components to probes.  

When you click on Add Component, the component you've selected 
(with the mouse) is added to the list of components for the 
currently selected probe.  If you try to add the component to the 
default probe, NWorks will create a new probe (called "untitled") 
to hold this component.  It will not allow you to add components 
to the default probe.

If you use the NEW button to create a new probe without specify-
ing a new name in the Name field, the new probe will automatical-
ly take the name "untitled".  Of course, you can change its name to 
anything you like through the RENAME button.  If you again create 
a new probe without specifying a name, this additional new probe 
will take the name "untitle0", and each unnamed new probe after 
this will increment the number at the end of "untitle".

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.

:graph@initialize
        --- Help about the Instrument/Initialize tool ---

Use this tool to "zero out" all data arrays associated with an 
instrument.  For strip charts, this tool will set the marker 
position to the start of the chart.

To initialize an instrument, simply click on it.
:graph@hide
             --- Help about the Graph/Hide tool ---

Use this tool to display an instrument in outline mode.  This is 
useful when there are many instruments and they obstruct network 
layers from being seen and selected.

To hide the instrument, click on it.

Use the Graph/Show tool to redisplay the instrument.
:graph@show
             --- Help about the Graph/Show tool ---

Use this tool to redisplay an instrument that's currently in 
outline mode. To redisplay the instrument, click on it.

:layertolayer@add
        --- Help about the Layer Connection/Add tool ---

Use this tool to connect source and destination layers.  After 
clicking on the source and destination layers, a dialog box will 
appear that allows you to specify:

  * Connection mode
  * Initial values
  * Connection weight types
  * Connection scope


Connection Mode

Pick one of the radio buttons:

Corresponding
            Connects PEs in the source layer to PEs in the 
            destination layer in corresponding positions.  

            This is the method for connecting a layer of proc-
            essing elements to themselves.

Full        Connects every PE in the source layer to every PE in 
            the destination layer.

Random      Provides random connections between the PEs in the 
            source layer to PEs in the destination layer.  You 
            control the density of the connections.  For 
            each possible connection, a uniformly distributed 
            random number between 0 and 1 is chosen. If the 
            number is less than the value in the connection 
            density dialog box, the connection is made (or 
            modified). If not it is skipped.

Percentage  You specify the percentage of possible connections 
            to make. Each processing element in the layer will 
            have exactly the same number of incoming connec-
            tions. The choice of the source for each connection 
            will be made at random from all possible source 
            connections.


Connection Weight Types

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 


:layertolayer@edit
        --- Help about the Layer Connection/Edit tool ---

Use this tool to edit all connections between source and destina-
tion layers.  After clicking on the source and destination lay-
ers, a dialog box will appear that allows you to modify:

  * Connection weight type
  * Initial weight values
  * Connection scope

IMPORTANT: It's quite possible that not all connections between 
layers will have the same state.  For instance, some may have 
different connection weight types, initial values and connection 
scope.  The functions from selected radio buttons in this dialog 
box will be applied to every connection between the designated 
layers.  If you don't want connections universally changed in any 
of the editing categories, click the No Change radio button.

The editing categories are:

Connection Weight Type

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.

No Change   Make no changes to the connection weight type.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

No Change   Make no changes to the connection weight type.


Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 

No Change   Make no changes to the connection weight type.


:layertolayer@delete
       --- Help about the Layer Connection/Delete tool ---

Use this tool to delete all connections between designated source 
and destination layers.  You will NOT be asked to confirm your 
decision to delete connections.  Once deleted, there is NO "undo" 
command.

:layertolayer@probe
       --- Help about the Layer Connection/Probe tool ---

Use this tool to add layer connections as probe components to new 
or existing probes.  When you click on Add Component, the compo-
nent you've selected (with the mouse) is added to the list of 
components for the currently selected probe.  If you try to add 
the component to the default probe, NWorks will create a new 
probe (called "untitled") to hold this component.  It will not 
allow you to add components to the default probe.

If you use the NEW button to create a new probe without specify-
ing a new name in the Name field, the new probe will automatical-
ly take the name "untitled".  Of course, you can change its name to 
anything you like through the RENAME button.  If you again create 
a new probe without specifying a name, this additional new probe 
will take the name "untitle0", and each unnamed new probe after 
this will increment the number at the end of "untitle".

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.

:layertolayer@initialize
     --- Help about the Layer Connection/Initialize tool ---

Use this tool to randomize variable weights leading into the 
destination layer from the source layer.  Fixed, set and modified 
weights are not affected.  Refer to the corresponding tool for
layers for more details on the randomization.

Initializing will zero all PEs in the destination layer and the
delta weights and auxiliary weights associated with each connection.

:layertolayer@randomizeweights
     --- Help about the Layer Connection/Randomize tool ---

Use this tool to randomize all of the variable weights leading 
into the destination layer from the source layer.  Fixed, set and 
modified weights are not affected.  Refer to the corresponding
tool for layers for more details on the randomization and on how
this tool differs from the Initialize tool.
:layertolayer@jogweights
    --- Help about the Layer Connection/Jog Weights tool ---

Use this tool to jog (add a random number) to all of the variable 
weights leading into a destination layer from a source layer.  
Refer to the corresponding tool for layers for more details.
multiplicatively post-normalized as appropriate.
:layertolayer@zerodeltaweights
 --- Help about the Layer Connection/Zero Delta Weights tool ---

Use this tool to zero the Delta Weights and auxiliary weights
of all variable connections leading into a designated destination
layer from a designated source layer.  Refer to the corresponding
tool for layers for more details.

:layertolayer@showconnections

  --- Help about the Layer Connection/Show Connections tool ---

Use this tool to display previously hidden connections from a 
designated source layer to a designated destination layer.  

For densely connected layers, the resulting screen update time 
can be quite extensive.  It is often helpful to Hide all connec-
tions (Network/Hide Connections command) and then selectively 
redisplay them on a processing element or layer basis.  

This command affects only the Network style of display.


:layertolayer@hideconnections
 --- Help about the Layer Connections/Hide Connections tool ---

Use this tool to hide connections from the designated source 
layer to the designated destination layer.  

Connections can be selectively redisplayed by using any of the 
Show Connections tools from the object palette, or through the 
Network Show Connections command.  

This command affects only the Network style of display.

:petope@add
          --- Help about the PE Connection/Add tool ---

Use this tool to connect source and destination PEs.  After 
clicking on the source and destination PEs, a dialog box will 
appear that allows you to specify:

  * Connection mode
  * Initial values
  * Connection weight types
  * Connection scope


Connection Mode

Pick one of the radio buttons:

Corresponding
            Connects the source PE to the destination PE only if 
            they are in corresponding positions.  

Full        Connects the source and destination PEs..

Random      Randomly completes the connections between the 
            source and destination PEs.  You control the density 
            of the connections.  For each possible connection, a 
            uniformly distributed random number between 0 and 1 
            is chosen. If the number is less than the value in 
            the connection density dialog box, the connection is 
            made (or modified). If not it is skipped.

Percentage  You specify the percentage that sets the likelihood 
            of the connection being made between the source and 
            destination PEs.


Connection Weight Types

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 

:petope@edit
          --- Help about the PE Connection/Add tool ---

Use this tool to add connections between individual PEs.  

  * Connection weight type
  * Initial weight values
  * Connection scope

IMPORTANT: It's quite possible that not all connections between 
PEs will have the same state.  For instance, some may have dif-
ferent connection weight types, initial values and connection 
scope.  The functions from selected radio buttons in this dialog 
box will be applied to every connection between the designated 
layers.  If you don't want connections universally changed in any 
of the editing categories, click the No Change radio button.

The editing categories are:

Connection Weight Type

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.

No Change   Make no changes to the connection weight type.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

No Change   Make no changes to the connection weight type.


Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 

No Change   Make no changes to the connection weight type.

:petope@clone
         --- Help about the PE Connection/Clone Tool ---

This tool clones a connection between a source PE and a destina-
tion PE for a selected target PE. If the connection is local, it 
is ignored. If it is absolute, the target processing element will 
be connected to the original source with the same weight as the 
absolute connection. If it is relative, the destination process-
ing element will be connected to the PE with the same relative 
position as the original source and with the same weight as the 
original source.

:petope@delete
         --- Help about the PE Connection/Delete Tool---

This tool lets you delete the connection between a source and 
destination PE.  An alert box will ask you to confirm the dele-
tion before removing the connection.  Make certain that you 
really want to remove the connection, as the only way to recover 
it is to reload the last saved version of the network.

:petope@probe
         --- Help about the PE Connection/Probe Tool ---

This tool allows you to add connections between PEs as components 
of probes; to edit, rename or delete a previously created probe; 
and to purge selected components from a probe. When you click on 
source and destination PEs to select connections, the Probe 
Select/Edit dialog box will appear.  

Rules to Remember

Each time you click on a layer with the Probe Tool, the Probe 
Select/Edit dialog box will appear.  You can select any existing 
probe, or create a new probe at this point.  When you click on 
Add Component, the component you've selected is added to the list 
of components for the currently selected probe.  If you attempt 
to add the component to the default probe, NWorks will create a 
new probe, called untitled, to hold this component (it will not 
allow you to add components to the default probe).

If you create a new probe (through the NEW button) without speci-
fying a new name in the Name field, the new probe will automati-
cally take the name untitled.  Of course, you can change its name 
to anything you like through the RENAME button.  If you again 
create a new probe without specifying a name, this additional new 
probe will take the name untitle0, and each unnamed new probe 
after this will increment the number at the end of untitle.

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.
:petope@initialize
      --- Help about the PE Connection/Initialize Tool ---

Use the Initialize tool to randomize the (variable) weight between 
the source and destination PEs you select.  Fixed, set and modi-
fied weights are not affected.    Refer to the corresponding tool for
layers for more details on the randomization.

Initializing will also zero the delta weight and any auxiliary weights
associated with the connection.
:petope@randomizeweights
   --- Help about the PE Connection/Randomize Weight Tool ---

Use the Randomize tool to randomize the (variable) weight between 
the source and destination PEs you select.  Fixed, set and modi-
fied weights are not affected.    Refer to the corresponding tool for
layers for more details on the randomization and for the differences
between "Randomize" and "Initialize".
:petope@jogweights
      --- Help about the PE Connection/Jog Weight Tool ---

Use this tool to specify a range for a random value to be added 
to a variable weight assigned to a connection between the desig-
nated source PE and the designated destination PE. It is useful 
to help get a network out of a local minima in weight space 
(especially back-propagation networks). When a weight is jogged, 
its associated delta weight is set to zero.

:petope@zerodeltaweights
   --- Help about the PE Connection/Zero Delta Weight Tool ---

Use this tool to zero the Delta Weight and any auxiliary weights
for the connection between a source and a destination PE.  It is
assumed the connection has a variable weight.
:petope@hideconnections
   ---- Help about the PE Connection/Hide Connection Tool ---

Use this tool to hide the connection from a specified source PE to 
a specified destination PE. For large or complex networks, hidden 
connections speed up redraw time as well as simplifying the display.

:petope@showconnections
    --- Help about the PE Connection/Show Connection Tool ---

Use this tool to display a previously hidden connection from a source 
PE to a destination PE.  

:fanin@add
             --- Help about the Fan-in/Add tool ---

Use this tool to create a "fan-in" connection between a designat-
ed source layer and destination PE.  A fan-in connection consists 
of connections from multiple PEs (usually all) in a source layer 
to a single PE in the destination layer.

After clicking on the source layer and destination PE, a dialog 
box will appear that allows you to specify:

  * Connection mode
  * Initial values
  * Connection weight types
  * Connection scope


Connection Mode

Pick one of the radio buttons:

Corresponding
            Connects PEs in the source layer to the destination 
            PE in corresponding positions.  

            This is the method for connecting a layer of proc-
            essing elements to themselves.

Full        Connects every PE in the source layer to the desti-
            nation PE.

Random      Provides random connections between the PEs in the 
            source layer to the destination PE.  You control the 
            density of the connections.  For each possible 
            connection, a uniformly distributed random number 
            between 0 and 1 is chosen. If the number is less 
            than the value in the connection density dialog box, 
            the connection is made (or modified). If not it is 
            skipped.

Percentage  You specify the percentage of possible connections 
            to make. Each processing element in the layer will 
            have exactly the same number of incoming connec-
            tions. The choice of the source for each connection 
            will be made at random from all possible source 
            connections.


Connection Weight Types

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 

:fanin@edit
        --- Help about the Fan-in/Edit tool ---

Use this tool to edit fan-in connections between a designated 
source layer and destination PE.  After clicking on the source 
layer and destination PE, a dialog box will appear that allows 
you to modify:

  * Connection weight type
  * Initial weight values
  * Connection scope

IMPORTANT: It's quite possible that not all connections in the 
fan-in will have the same state.  For instance, some may have 
different connection weight types, initial values and connection 
scope.  The functions from selected radio buttons in this dialog 
box will be applied to every connection between the designated 
layers.  If you don't want connections universally changed in any 
of the editing categories, click the No Change radio button.

The editing categories are:

Connection Weight Type

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.

No Change   Make no changes to the connection weight type.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

No Change   Make no changes to the connection weight type.


Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 

No Change   Make no changes to the connection weight type.


:fanin@delete
            --- Help about the Fan-in/Delete tool ---

Use this tool to delete all connections between a designated 
source layer and a designated destination PE.  You will NOT be 
asked to confirm your decision to delete connections.  Once 
deleted, there is NO "undo" command.

:fanin@probe
            --- Help about the Fan-in/Probe tool ---

Use this tool to add fan-in connections as probe components to 
new or existing probes.  When you click on Add Component, the 
component you've selected (with the mouse) is added to the list 
of components for the currently selected probe.  If you try to 
add the component to the default probe, NWorks will create a new 
probe (called "untitled") to hold this component.  It will not 
allow you to add components to the default probe.

If you use the NEW button to create a new probe without specify-
ing a new name in the Name field, the new probe will automatical-
ly take the name "untitled".  Of course, you can change its name to 
anything you like through the RENAME button.  If you again create 
a new probe without specifying a name, this additional new probe 
will take the name "untitle0", and each unnamed new probe after 
this will increment the number at the end of "untitle".

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.

:fanin@initialize
          --- Help about the Fan-in/Initialize tool ---

Use this tool to randomize variable weights leading into the 
destination PE from the source layer.  Fixed, set and modified 
weights are not affected.  Refer to the corresponding tool for
layers for more details on the randomization.

Initializing will zero all PEs in the layer and the delta weights 
and auxiliary weights associated with each connection.
:fanin@randomizeweights
          --- Help about the Fan-in/Randomize tool ---

Use this tool to randomize all of the variable weights leading 
into the destination PE from the source layer.  Fixed, set and 
modified weights are not affected.  Refer to the corresponding
tool for layers for more details on the randomization and on how
this tool differs from the Initialize tool.
:fanin@jogweights
         --- Help about the Fan-in/Jog Weights tool ---

Use this tool to zero the Delta Weights and auxiliary weights
of all variable connections leading into a designated destination
PE from a designated source layer.  Refer to the corresponding
tool for layers for more details.

:fanin@zerodeltaweights
      --- Help about the Fan-in/Zero Delta Weights tool ---

Use this tool to zero the Delta Weights and auxiliary weights
of all variable connections leading into a designated destination
PE from a designated source layer.  Refer to the corresponding
tool for layers for more details.
:fanin@showconnections

       --- Help about the Fan-in/Show Connections tool ---

Use this tool to display previously hidden connections from a 
designated source layer to a designated destination PE.  

For densely connected layers, the resulting screen update time 
can be quite extensive.  It is often helpful to Hide all connec-
tions (Network/Hide Connections command) and then selectively 
redisplay them on a processing element or layer basis.  

This command affects only the Network style of display.


:fanin@hideconnections
       --- Help about the Fan-in/Hide Connections tool ---

Use this tool to hide connections from the designated source 
layer to the designated destination PE

Connections can be selectively redisplayed by using any of the 
Show Connections tools from the object palette, or through the 
Network Show Connections command.  

This command affects only the Network style of display.
:fanout@add
             --- Help about the Fan-out/Add tool ---

Use this tool to create a "fan-out" connection between designated 
source and destination layer.  A fan-out connection consists of 
connections from a source PE to a multiple PEs (usually all) in 
the destination layer.

After clicking on the source PE and destination layer, a dialog 
box will appear that allows you to specify:

  * Connection mode
  * Initial values
  * Connection weight types
  * Connection scope


Connection Mode

Pick one of the radio buttons:

Corresponding
            Connects the source PE to PEs in the destination 
            layer in corresponding positions.  

            This is the method for connecting a layer of proc-
            essing elements to themselves.

Full        Connects the source PE to every PE in the destina-
            tion layer.

Random      Provides random connections between the source PE to 
            PEs in the destination layer.  You control the 
            density of the connections.  For each possible 
            connection, a uniformly distributed random number 
            between 0 and 1 is chosen. If the number is less 
            than the value in the connection density dialog box, 
            the connection is made (or modified). If not it is 
            skipped.

Percentage  You specify the percentage of possible connections 
            to make. Each processing element in the layer will 
            have exactly the same number of incoming connec-
            tions. The choice of the source for each connection 
            will be made at random from all possible source 
            connections.


Connection Weight Types

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.


Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 
:fanout@edit
        --- Help about the Fan-out/Edit tool ---

Use this tool to edit fan-out connections between a designated 
source PE and destination layer.  After clicking on the source PE 
and destination layer, a dialog box will appear that allows you 
to modify:

  * Connection weight type
  * Initial weight values
  * Connection scope

IMPORTANT: It's quite possible that not all connections in the 
fan-out will have the same state.  For instance, some may have 
different connection weight types, initial values and connection 
scope.  The functions from selected radio buttons in this dialog 
box will be applied to every connection between the designated 
layers.  If you don't want connections universally changed in any 
of the editing categories, click the No Change radio button.

The editing categories are:

Connection Weight Type

Variable Weights
            This choice allows the selected weight(s) to learn. 
            See "An Introduction to Neural Computing" in the 
            Neural Computing volume for a description of how 
            such learning occurs. See the section on "Learning 
            Rules in Processing Element Architecture" in the 
            NeuralWorks Reference Guide for a detailed descrip-
            tion of the learning rules supported by NWorks.

Fixed Weights
            This option sets a fixed weight so that the weight 
            of the connection will not change. It will not be 
            affected by learning, randomizing, zeroing or 
            jogging weights. Fixed weights are shown graphically 
            by a small anchor in the middle of the connecting 
            weight. 

Set Weights This option makes the connections form set weights. 
            These are used in special ways to transfer informa-
            tion between portions of a network. See the "Proc-
            essing Element Architecture" chapter in Neural 
            Computing for a detailed discussion of set weights. 
            The effective value of a set weight is the product 
            of the weight and the Input Clamp value from the 
            Learning and Recall Schedule. Set weights are indi-
            cated graphically with a small dial and arrow 
            through the center of the weight.

Modified Weights
            Modified weights are always multiplied by the Mod 
            Factor in the Learning and Recall Schedule. Their 
            effective value is the product of the Mod Factor and 
            the weight. Modified weights are graphically illus-
            trated by a small triangle at their centers.  

Disabled Weights
            It is also possible to disable weights by selecting 
            the disable option. In this case, the network per-
            forms as if the connection did not exist.

No Change   Make no changes to the connection weight type.

Initial Weight Values

Click one of the radio buttons. The button functions are:

Value       Set all of the weights to the value specified in the 
            text field.

Range       Randomize the weights within a range specified in 
            the text fields.

Initialize  Initialize the weights according to the range speci-
            fied by the Low/Init and High/Init values in the 
            Layer/Edit tool dialog box.

No Change   Make no changes to the connection weight type.

Connection Scope

Click one of the radio buttons to select a scope.

Absolute    Connections are always made from the same source 
            processing element. 

Relative    Connections are always relative to the destination 
            processing element (e.g.: one layer down and two 
            processing elements before). 

Local       Connections are local to this processing element. 

No Change   Make no changes to the connection weight type.
:fanout@delete
            --- Help about the Fan-out/Delete tool ---

Use this tool to delete all connections between a designated 
source PE and a designated destination layer.  You will NOT be 
asked to confirm your decision to delete connections.  Once 
deleted, there is NO "undo" command.
:fanout@probe
            --- Help about the Fan-out/Probe tool ---

Use this tool to add fan-out connections as probe components to 
new or existing probes.  When you click on Add Component, the 
component you've selected (with the mouse) is added to the list 
of components for the currently selected probe.  If you try to 
add the component to the default probe, NWorks will create a new 
probe (called "untitled") to hold this component.  It will not 
allow you to add components to the default probe.

If you use the NEW button to create a new probe without specify-
ing a new name in the Name field, the new probe will automatical-
ly take the name "untitled".  Of course, you can change its name 
to anything you like through the RENAME button.  If you again 
create a new probe without specifying a name, this additional new 
probe will take the name "untitle0", and each unnamed new probe 
after this will increment the number at the end of "untitle".

If you add a component to a probe, and then decide that you don't 
really want that component attached to that probe, you must PURGE 
it from the list.  Once a component has been added to the list, 
clicking on Cancel won't remove it.

The command buttons have the following functions:

EDIT        Specifies that the currently selected probe can now 
            be edited.  The probe you are editing has its compo-
            nents shown in the list.

NEW         Creates a new probe.  The probe takes the name 
            specified in the Name field unless a probe with that 
            name already exists.  If there is a conflict with an 
            existing name, the new probe is given the name 
            "untitled".

PURGE       Deletes the currently highlighted components in the 
            probe component (Type Source Destination) scroll 
            box.

DELETE      Deletes the currently selected probe from the list 
            of probes.

RENAME      Allows you to rename the currently selected probe to 
            the name in the Name field.

Add Component
            Add Component adds the probe component you just 
            selected with the Probe tool to the list of compo-
            nents.  The component will be appended to the bottom 
            of the list.
:fanout@initialize
          --- Help about the Fan-out/Initialize tool ---

Use this tool to randomize variable weights leading into the 
destination layer from the source PE.  Fixed, set and modified 
weights are not affected.  Refer to the corresponding tool for
layers for more details on the randomization.

Initializing will zero all PEs in the destination layer and the
delta weights and auxiliary weights associated with each
variable connection.
:fanout@randomizeweights
          --- Help about the Fan-out/Randomize tool ---

Use this tool to randomize all of the variable weights leading 
into the destination layer from the source PE.  Fixed, set and 
modified weights are not affected.  Refer to the corresponding
tool for layers for more details on the randomization and on how
this tool differs from the Initialize tool.
:fanout@jogweights
         --- Help about the Fan-out/Jog Weights tool ---

Use this tool to jog (add a random number) to all of the variable 
weights leading into a destination layer from a source PE.
Refer to the corresponding tool for layers for more details.
multiplicatively post-normalized as appropriate.
:fanout@zerodeltaweights
 --- Help about the Fan-out/Zero Delta Weights tool ---

Use this tool to zero the Delta Weights and auxiliary weights
of all variable connections leading into a designated destination
layer from a designated source PE.  Refer to the corresponding
tool for layers for more details.
:fanout@showconnections

       --- Help about the Fan-out/Show Connections tool ---

Use this tool to display previously hidden connections from a 
designated source PE to a designated destination layer.  

For densely connected layers, the resulting screen update time 
can be quite extensive.  It is often helpful to Hide all connec-
tions (Network/Hide Connections command) and then selectively 
redisplay them on a processing element or layer basis.  

This command affects only the Network style of display.
:fanout@hideconnections
      --- Help about the Fan-outs/Hide Connections tool ---

Use this tool to hide connections from the designated source PE 
to the designated destination layer.

Connections can be selectively redisplayed by using any of the 
Show Connections tools from the object palette, or through the 
Network Show Connections command.  

This command affects only the Network style of display.
:fanout@clone
            --- Help about the Fanout/Clone tool ---

Use this tool to clone the connections to a source PE (that you 
select) to all of the PEs in a destination layer.  It replicates 
these connections differently depending on the scope.  For in-
stance, Local connections are not cloned at all.  Absolute con-
nections are cloned, but the output PE remains the same as for 
the connection(s) for the source PE. Relative positions are 
cloned, but maintain the same geometry.  Refer to the "Fan-out 
Tool" chapter in the Reference Guide for more details.


