Copyright: ©1997-2001, NeuroDimension, Inc.


About On-line Help

NeuroSolutions for Excel

Program version:	4.00
Help version:	4.00
Help Release Date:	01/18/01
Download the latest release from:	http://www.nd.com/support/help.htm


What is NeuroSolutions for Excel?

NeuroSolutions for Excel is a revolutionary product that allows you access to all the powerful features of NeuroSolutions, but with the convenience and ease of use of a spreadsheet program.  This Microsoft Excel add-in gives you the ability to visually tag your data as Training, Cross Validation, Testing, or Production, train a neural network, and test the neural network's performance directly from within a Microsoft Excel worksheet.  Reports are automatically generated showing the results.  Working with neural networks could not be any easier.

NeuroSolutions for Excel is not just for the novice, however.  There are also powerful built-in features that help you to find the optimum neural network for your problem.  These include the ability to train a neural network multiple times, vary any neural network parameters across multiple runs, genetically optimize network parameters, and create your own custom batches.


Contacting NeuroDimension

NeuroDimension, Inc.

1800 N. Main Street, Suite D4

Gainesville, FL 32609

http://www.nd.com/

Sales and Information

Sales	1-800-634-3327 (Option 3)
Product Literature and Evaluation Software	1-800-634-3327 (Option 2)
Fax	352-377-9009
Email	info@nd.com
Calls Outside U.S.	352-377-5144
Technical Support

Bug Reports, Installation Problems, and Priority Support	1-800-634-3327 (Option 0)
All Other Technical Support	352-377-1542
Fax	352-377-9009
Email	nsforexcel_support@nd.com
Calls Outside U.S.	352-377-1542

Before contacting technical support, please attempt to answer any questions by first consulting the following resources:

•	The printed manual (if applicable)

•	The on-line help

•	The Frequently Asked Questions (FAQ)

The latest versions of the on-line help and FAQ can always be found at the NeuroDimension web site: www.nd.com


Technical Support

Included with Evaluation/Demo Software

•	Toll-free line for bug reports and installation problems

Included with Purchased Software

•	Toll-free line for bug reports and installation problems

•	1 year unlimited email, fax and phone support (toll line)

Contact Information

Bug Reports, Installation Problems and Priority Support	1-800-634-3327 (Option 0)
All other Technical Support	(352) 377-1542
Fax	(352) 377-9009
Email	nsforexcel_support@nd.com
Calls Outside U.S.	(352) 377-1542
Please have your invoice number ready when calling and include your invoice number in all fax, email, or written correspondence.

Note that the technical support described above is for questions regarding the software package. Neural network experts are on staff and available for consulting on an hourly basis. Consulting rates are dependent on the specifics of the problem.


Other NeuroDimension Products

NeuroSolutions

NeuroSolutions is the most powerful and flexible neural network simulator available for the Windows market. This leading edge software combines a modular, icon-based network design interface with advanced learning procedures, such as recurrent backpropagation and backpropagation through time. NeuroSolutions can be used to solve real-world problems such as financial forecasting, speech recognition, process control, and many more. Notable features include C++ code generation, user-defined algorithms and integration with Excel.

Custom Solution Wizard

The Custom Solution Wizard is a tool that will take an existing neural network created with NeuroSolutions and automatically generate and compile a Dynamic Link Library (DLL). This allows you to easily incorporate neural network models into your own applications and into other NeuroDimension products, such as TradingSolutions (Developers level only).

While using the wizard to create the DLL, you are also given the option of creating a shell for any of the following programming environments:

• Visual Basic®

• Visual C++®

• Microsoft Excel®

• Microsoft Access®

• Active Server Pages (Developers level only)

Each shell provides a sample application along with source code to give you a starting point for integrating the generated DLL into your application.

The generated neural network DLL provides a simple protocol for assigning the network input and producing the corresponding network output.  Furthermore, the Developers level of the Custom Solution Wizard supports learning.  This allows you to train the generated neural network and/or retune the network after gathering new data.  Embedding a custom neural network into your application could not be any easier!

Neural and Adaptive Systems: Fundamentals Through Simulations

This interactive electronic book published by John Wiley and Sons combines the hypertext and searching capabilities of the Windows help system with the highly graphical simulation environment of NeuroSolutions to produce a revolutionary learning tool. The book contains over 200 interactive experiments in NeuroSolutions to elucidate the fundamentals of neural networks and adaptive systems.

TradingSolutions

TradingSolutions is a comprehensive financial analysis software package that helps you make better trading decisions. It combines traditional technical analysis with state-of-the-art artificial intelligence technologies. Now you can use any combination of financial indicators in conjunction with advanced neural networks and genetic algorithms to create models that are remarkably effective, especially in today's volatile markets.

TradingSolutions' user-friendly interface allows you to perform complex financial forecasting without leaving you lost in the technology. Simple wizards guide you step-by-step through each task, while optional advanced panels give you the flexibility to adjust parameters behind the scenes.

Genetic Server

Genetic Server is a general-purpose genetic algorithm ActiveX component that can be used to easily build custom genetic applications in Visual Basic (or any other environment that supports Active-X components).  This software provides two core genetic algorithms:  Generational and Steady State.  Furthermore, the user can select between several types of selection, crossover, and mutation.  Floating point and integer data types are supported in addition to the traditional binary data type.  Genetic Server also provides several methods for terminating the genetic search.

Genetic Library

Genetic Library is a general-purpose genetic algorithm library written in ANSI C++.  The library provides two core genetic algorithms:  Generational and Steady State.  Furthermore, the user can select between several types of selection, crossover, and mutation, or even write their own.  Floating point and integer data types are supported in addition to the traditional binary data type.  The library also provides several methods for terminating the genetic search.

Consulting

Neural network and genetic algorithm experts are on staff and available for consulting on an hourly basis.  Consulting rates are dependent upon the specifics of the problem.  To obtain an estimate for consulting, please email your problem specifics to info@nd.com.

Priority Support Package

The priority support package is a valuable service that can be added to the purchase of any NeuroDimension product anytime within the first 30 days of product purchase. This service provides preferred status on all support issues as well as the following benefits:

•	Toll-free line for all calls

•	One FREE major upgrade

•	FREE minor upgrades

The cost for the priority support package is 30% of the purchase price of the product to be covered.

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® Visual Basic, Visual C++, Microsoft Excel, and Microsoft Access are registered trademarks of Microsoft Corporation


Pricing Information

The latest pricing for all NeuroDimension products can be found on our web site at: www.nd.com/pricing.htm.

Note: If you do not have access to the internet, see the Contacting NeuroDimension topic for information on how to contact NeuroDimension via phone, fax and mail.


Ordering Information

NeuroDimension products can be ordered using any of the following 3 methods:

1	Place the order on-line using our SECURE order entry system.

2	Download and print the latest order form then fax the completed form to 352-377-9009, or mail it to:

NeuroDimension, Inc.

Order Processing Department

1800 N. Main Street, Suite #D4

Gainesville, FL 32609-8606

3	Phone in your order (800-634-3327 or 352-377-5144) Monday through Friday between the hours of 8:30 AM and 5:00 PM EST.

We accept payment by credit card (Visa, MasterCard or American Express), wire transfer, or prepayment by check or money order.

Note: In order to use the links within this topic, you must be connected to the internet.


System Requirements

In order to run NeuroSolutions for Excel, you must meet the following system requirements:

1	Windows 95/98/Me/NT4/2000

2	Microsoft Excel 97/2000

3	NeuroSolutions 4


Running NeuroSolutions for Excel

There are two primary ways to run NeuroSolutions for Excel:

1	Click the “NeuroSolutions for Excel” menu item located within the “NeuroSolutions 4 | NeuroSolutions for Excel” folder of the “Start” menu.

2	Click the “NS Excel” button located on the “Necessities” toolbar within NeuroSolutions.

Portion of the Necessities toolbar containing the NS Excel button

Both of these methods will launch Microsoft Excel and load the NeuroSolutions for Excel add-in. A “NeuroSolutions” menu will appear in the menu bar of Microsoft Excel. This is the menu you will use to perform most of the NeuroSolutions for Excel operations.

You can also perform column and row operations by right-clicking on one or more columns or rows. The appropriate NeuroSolutions for Excel menu items will appear on the resulting shortcut menu. Shortcut menus provide a faster way to perform an operation by displaying operations available in the current context (selection).

Tagging rows as Training using row shortcut menu

Tagging columns as Desired using column shortcut menu

Note: If you have both Excel 97 and Excel 2000 installed, NeuroSolutions for Excel will use Excel 2000. If you would like to use Excel 97 instead, first open Excel 97 then run NeuroSolutions for Excel using one of the methods above.


Installing within Microsoft Excel

This topic describes how to install NeuroSolutions for Excel within Microsoft Excel such that NeuroSolutions for Excel will automatically run each time Microsoft Excel is opened. This is NOT the recommended method for running NeuroSolutions for Excel. In general you will want to run NeuroSolutions for Excel using one of the methods described in the Running NeuroSolutions for Excel topic.

Installation Steps

1	Run NeuroSolutions for Excel.

2	From the Tools menu of Microsoft Excel, select Add-Ins.

3	Check the “NeuroSolutions for Excel” item.

4	Click the OK button on the Add-Ins dialog box.


NeuroSolutions for Excel Organization

NeuroSolutions for Excel is broken down into 8 major modules.  These are

1	Preprocess Data

Used to apply various preprocessing techniques to the raw data to prepare it for input into the neural network.

2	Analyze Data

Used to obtain information about the data during the preprocessing stage or to analyze the network output.

3	Tag Data

Used to graphically tag (designate) portions of the data as Training Input, Training Desired, Cross Validation Input, Cross Validation Desired, Testing Input, Testing Desired, and Production Input.

4	Create Data Files

Used to create tab a delimited ASCII file for each cross-section tagged using the Tag Data module.

5	Create/Open Network

Used to create a NeuroSolutions breadboard (neural network) from scratch by utilizing the NeuralBuilder wizard or to open an existing NeuroSolutions breadboard.

6	Train Network

Used to train a network using one of several built-in training processes.  This powerful module permits the user to easily find the optimum network for a particular problem.

7	Test Network

Used to test a network after the optimum network has been found using the Train Network module.  In testing the network, various performance measures are computed.  This module can also be used to perform sensitivity analysis on the trained network.

8	Apply Production Dataset

Used to feed new input data (data tagged as Production Input) to the network and compute the network output.  This is how the trained neural model is applied to new data where the output is unknown.

Each of these modules is a menu / menu item under first partition of the main NeuroSolutions menu.

NeuroSolutions menu in Microsoft Excel


Using NeuroSolutions for Excel

NeuroSolutions for Excel was designed to allow you to develop a complete solution to your problem in one easy to use package while also giving you the flexibility to customize its operation using Visual Basic for Applications as a scripting language.  The following figure presents a flow diagram describing the order in which the NeuroSolutions for Excel modules can be used to solve your problem.  Notice that, at a minimum, only three operations are required: Tag Data, Create/Open Network, and Train Network.  These operations are usually followed up by testing the models performance (Test Network) and applying the model to new input data where the output is unknown (Apply Production Dataset).

Flow diagram illustrating the suggested usage of NeuroSolutions for Excel


Data Requirements

There are a few data requirements that must be met.  These are as follows:

1	All columns of data must contain labels (see Insert Column Labels).

2	All labels for a particular column tag (“Input” or “Desired”) must be unique.

3	The labels must be located in the first row of the worksheet.

4	The data must be arranged in one contiguous rectangular block (with the exception of the Production dataset in which Desired data is not required).

5	There can be no empty cells within the tagged cross-sections (see Clean Data).

6	The data must start in the first column and the second row (directly after the labels).

Example of valid data


Selecting Columns and Rows

To select an entire column, click on the letter for the column you want to select.  For example, if you want to select the first column, you click on the letter “A” located above the column.  The entire column will be highlighted to denote that it is selected.  (Note: If no row or column headers are present, you must enable the row and column headers under Tools|Options|View).

If you want to select multiple contiguous columns, click on the letter for the first column you want to select and hold the mouse button down as you drag the cursor across the letters for the other columns you want to select.

If you want to select multiple non-contiguous columns, click on the letter for the first column you want to select, hold down the Ctrl button, and click on the letters for the other columns you want to select.

To select  an entire row, use the same procedure as discussed above for selecting entire columns, except click on the row number located to the left of the corresponding row.

The following two figures illustrate the selection of multiple non-contiguous columns and multiple contiguous rows, respectively.

Selection of multiple non-contiguous columns

Selection of multiple contiguous rows


Status Bar

The status bar in Microsoft Excel is used to provide status during the execution of some of the lengthy NeuroSolutions for Excel functions.  The actual information provided depends upon the function being executed.  To enable the Microsoft Excel status bar, make sure the Status Bar menu item (under the View menu) is checked.

Note: The status for the training procedures is provided on a dialog window that gets displayed when the procedure is run.  The execution of the training procedure can also be stopped from this dialog.


Preprocess Data Module

Menu Item	Description

Difference	Computes the difference or percent difference along a column of data.

Randomize Rows	Randomly arranges the rows of data and writes the result to a new sheet.

Sample	Creates a new worksheet made up of every Nth row of data within the active worksheet.

Moving Average	Computes the moving average of a column using the chosen window length.

Translate Symbolic Columns 	Translates columns that have been tagged as symbol.

Insert Column Labels 	Inserts a row of column labels into the first row of the active worksheet.

Clean Data 	Cleans the data by replacing blank cells, error codes, and/or user-defined text with an interpolated value, the column average, a random value, or the closest value in a column.

Run Batch 	Displays a dialog box showing the available Preprocess Data batches along with a description.  From this dialog, you choose which batch process to run.


Difference

Description

This menu item is used to find the difference or percent difference of a column of data.  Before selecting this operation, you must select a single column or multiple columns.  If multiple columns are selected, they may be contiguous or non-contiguous.  Note: To properly select a column, the entire column must be selected (see Selecting Columns and Rows).  The results of the differencing operation will be written starting at the first blank column in the current worksheet.

Dialog Box Options

Difference

Choose this option if you want to compute the difference between data values in a column.

Percent Difference

Choose this option if you want to compute the percent difference between data values in a column.


Randomize Rows

Description

Selection of this menu item will result in the creation of a new worksheet named “<Active Worksheet Name> Randomized” where <Active Worksheet Name> is the name of the active worksheet when this menu item was selected.  All of the data on the active worksheet will be copied to this new worksheet.  The rows of data on this new worksheet will then be randomized.  If the original data had already been tagged, the column tags will be maintained, but the row tags will be cleared.

This function is useful if you want to randomly tag exemplars (rows) within your data.  To accomplish this task you first run the randomize rows function.  Then within the “Randomized Data” worksheet, you tag the rows in blocks (either manually using Row(s) As Training, Row(s) As Cross Validation, or Row(s) As Testing, or automatically using Rows By Percentages).  This process is equivalent to randomly tagging rows within your original data worksheet.


Sample

Description

This menu item is used to periodically sample your data.  Selection of this menu item will result in the creation of a new worksheet named “<Active Worksheet Name> Sampled” where <Active Worksheet Name> is the name of the active worksheet when this menu item was selected.  The labels and the first row of data followed by every Nth row of data will be written to this new worksheet.

Dialog Box Options

Sample Every

Enter the sampling period into this edit box.


Moving Average

Description

This menu item is used to find the moving average of a column of data.  Before selecting this operation, you must select a single column or multiple columns.  If multiple columns are selected, they may be contiguous or non-contiguous.  Note: To properly select a column, the entire column must be selected (see Selecting Columns and Rows).

The moving average is the average value of a variable computed using a sliding window.  A moving average provides trend information that would not be evident in the average over all of a variables historical data.  The moving average of the selected column will be written in the first blank column in the current worksheet.

Dialog Box Options

Window Length

Enter the number of data values to use for computing each average value.


Translate Symbolic Columns

Description

Selection of this menu item will result in the translation of all columns tagged as Symbol.  A new worksheet named “<Active Worksheet Name> Translated” (where <Active Worksheet Name> is the name of the active worksheet when this menu item was selected) will be created.  The columns of data on the original worksheet will be copied column by column to the new worksheet.  When this procedure encounters a column tagged as Symbol, this column will first be translated (using unary encoding) and the result of this translation will be copied to the new worksheet.

Note: When a column of data is translated using unary encoding, one column is created for each unique symbol.  The label for each of the translated columns becomes “<Original Column Label> (<Symbol>)” where <Original Column Label> is the label of the original column and <Symbol> is the symbol that the 1’s in the translated column represents.  Within a translated column, a “1 “ signifies the existence of the corresponding symbol and a “0” signifies the existence of any other symbol.

Below are two examples of symbol translation:

X-Or data before symbol translation

X-Or data after symbol translation

Gender Classification Data before translation

Gender Classification Data after translation


Insert Column Labels

Description

Selection of this menu item will result in the insertion of a row of unique column labels.  The column labels will be inserted into the first row of the active worksheet.  The naming convention used for these labels is as follows:

1	Columns tagged as Input are labeled sequentially as Input1, Input2, Input3, etc.

2	Columns tagged as Desired are labeled sequentially as Desired1, Desired 2, Desired 3, etc.

3	Untagged columns are labeled as Column# where # is the actual column number.


Clean Data

Description

This menu item is used to search for and replace invalid data.  If your data contains missing values, Excel error codes, and/or text values, this operation can be used to replace these invalid values. The invalid values can be replaced using interpolation, the column average, a random value within the specified range, or the closest value in a column.

Dialog Box Options

Data Range

Enter the range of the data you want to clean. The range will can be automatically entered by clicking inside the Data Range text box followed by using the mouse to select a range of cells on the data worksheet. The default range is the entire range of data found on the active sheet.

Auto Select

Click to restore the Data Range to the default range (the contiguous block of data starting in cell A1 and extending outward).

Find what

Choose the type of invalid data to find from one of the following choices:

1	Blank Cells – Cells that don’t contain data.

2	Error Codes – Cells that contain Excel error codes such as “#DIV/0!”.

3	User Specified Text – Cells that contain the textual data specified in the “Text to Find” text box.

4	All – Cells that contain all of the above 3 types of invalid data.

Text to find

Enter the invalid text to search for. Available only when the “Find what” choice is set to “User Specified Text” or “All”.

Match Case

Check this box to make the text searching for the “User Specified Text” or “All” options case sensitive (i.e. the text in the “Text to Find” text box must match exactly).

Replace with

Choose the method to use for replacing the invalid data from one of the following choices:

1	Interpolation – Replaces invalid data with linearly interpolated values. To perform linear interpolation, the slope between the two closest valid values surrounding the invalid value is calculated. The interpolated value is then determined by adding the slope times the distance (from the first valid value) to the first valid value. For example, given a value of 2 in cell A2 and a value of 27 in cell A7, the slope would be 5 = [27 - 2] / [7 - 2], and the interpolated values for cells A3, A4, A5 and A6 would be 7, 12, 17, and 22, respectively. Note: Invalid values found at the endpoints (i.e. not between two valid values) are extrapolated in a manner similar to interpolation.

2	Column Average – Replaces invalid data with the average of all the numeric values in the column in which the invalid data resides.

3	Random Value – Replaces invalid data with a random value generated within a user-specified range.

4	Closest Value In Column – Replaces invalid data with the nearest valid value (in terms of proximity) in the column in which the invalid data resides.

Min

Enter the minimum bound to use for the random number generation when the “Replace with” method is set to “Random Value”.

Max

Enter the maximum bound to use for the random number generation when the “Replace with” method is set to “Random Value”.


Run Batch (Preprocess Data)

Description

Selection of this menu item will display a dialog box listing all of the custom Preprocess Data batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Preprocess Data batches displayed, click on its name then click the OK button.

A Preprocess Data batch is identified by its Module name.  All modules beginning with the prefix “NSEPreprocess” are interpreted as Preprocess Data batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.


Analyze Data Module

Menu Item	Description

Correlation	Computes the correlation between the each of the columns of data on the active worksheet.

Time Series Plot	Creates a Times Series Plot of the selected columns.

XY Scatter Plot	Creates an XY Scatter Plot of the selected columns.

Histogram	Computes the histogram of a selected column of data.

Summary Statistics	Computes various statistics for a selected column of data.

Trend Accuracy 	Compute the trend accuracy of the selected columns.

Run Batch 	Displays a dialog box showing the available Analyze Data batches along with a description.  From this dialog, you choose which batch process to run.


Correlation

Description

Measures the relationship between columns of data.  The returned correlation between two columns will be near 1 if the data in the two columns tend to move together, near -1 if the data in the two columns tend to move in inverse directions, or near 0 if the data in the two columns are unrelated.  The computed correlations are presented in a table format within a new worksheet named “<Active Worksheet Name> Correlation” where <Active Worksheet Name> is the name of the active worksheet when this menu item was selected.


Time Series Plot

Description

Creates a time series plot of the selected column(s) of data.  If multiple columns are selected, they can be non-contiguous.  The plot will be created on the active worksheet.


XY Scatter Plot

Description

Creates an XY scatter plot of the selected column(s) of data.  The first column of data will be used for the x-axis.  The remaining column(s) will be used as y-axis traces.  At least two columns of data must be selected (1 for the x-axis and 1 for the y-axis).  The selected columns can be non-contiguous.  The plot will be created on the active worksheet.


Histogram

Description

This menu item is used to find the histogram of a column of data.  Before selecting this operation, you must select a single column of data.  The histogram is calculated by counting the number of times (frequency) that a data value within the chosen column falls between a set of data bins equally spaced over the entire data range.  This provides a useful indication of the relative distribution of the data values.  The results are written to a new worksheet named “<Active Worksheet Name> Histogram” where <Active Worksheet Name> is the name of the active worksheet when this menu item was selected.  By default, the results are presented in a table, but can also be plotted.

Dialog Box Options

Sorted Histogram

Check this box if you want to include a column in the output table which presents the result in descending order of frequency.

Cumulative Percentage

Check this box if you want to include a column in the output table containing the cumulative percentages.

Chart Output

Check this box if you want the function to produce a plot of the histogram.  If the Cumulative Percentage box is checked, it will also be plotted.


Summary Statistics

Description

Computes several key statistics for the selected column(s) of data.  The column(s) must be selected before clicking this menu item.  Multiple columns can be selected, but they must be contiguous.  The default statistics computed are Mean, Standard Error (of the mean), Median, Mode, Standard Deviation, Variance, Kurtosis, Skewness, Range, Minimum, Maximum, Sum, and Count.  The results are presented in a tabular format.  The results are written to a new worksheet named “<Active Worksheet Name>  “Statistics” where <Active Worksheet Name> is the name of the active worksheet when this menu item was selected.

Dialog Box Options

Mean Confidence Level (in %)

Check this box if you want to include a row in the output table for the mean confidence level.  Then enter the confidence level you want to use into the corresponding edit box.

Kth Largest

Check this box if you want to include a row in the output table which reports the kth largest value.  In the corresponding edit box, enter the number to use for k.

Kth Smallest

Check this box if you want to include a row in the output table which reports the kth smallest value.  In the corresponding edit box, enter the number to use for k.


Trend Accuracy

Description

This menu item computes the trend accuracy of the selected columns of data.  Exactly two columns must be selected before clicking this menu item.  The first column of data selected will be interpreted as the desired network output and the second column of data selected will be interpreted as the actual network output.  The two selected columns do not have to be contiguous.

Important:  The interpretation of a column as the desired network output or the actual network output is based on the order in which the columns were selected, not the physical location of the columns.

The trend accuracy measure is useful when working with time series data.  It gives the percentage for which the actual output changed in the correct direction relative to the previous desired value.  The following example demonstrates how Trend Accuracy is calculated.

Calculation of Trend Accuracy


Run Batch (Analyze Data)

Description

Selection of this menu item will display a dialog box listing all of the custom Analyze Data batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Analyze Data batches displayed, click on its name then click the OK button.

An Analyze Data batch is identified by its Module name.  All modules beginning with the prefix “NSEAnalyze” are interpreted as Analyze Data batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.


Tag Data Module

Menu Item	Description

Column(s) As Input	Tags the selected column(s) of data as Input.

Column(s) As Desired	Tags the selected column(s) of data as Desired.

Column(s) As Symbol 	Tags the selected column(s) of data as Symbol.

Row(s) As Training	Tags the selected row(s) of data as Training.

Row(s) As Cross Validation	Tags the selected row(s) of data as Cross Validation.

Row(s) As Testing	Tags the selected row(s) of data as Testing.

Row(s) As Production 	Tags the selected row(s) of data as Production.

All Columns As Input	Tags all columns as Input.

All Non-Numeric Columns As Symbol 	Tags all non-numeric columns as symbol.

All Rows As Training	Tags all rows as Training.

Rows By Percentages	Tags the rows of data within the active worksheet as Training, Cross Validation, and Testing according to user-defined percentages.

Clear Tags	Allows you to clear any existing tag.

Clear Column Tag	Clears the tag(s) of the selected column(s).

Clear Symbol Tag 	Clears the symbol tag for the selected column(s).

Clear Row Tag	Clear the tag(s) of the selected row(s).

Clear All Tags	Clears all of the tags on the active worksheet.

Select Cross-Section	Allows you to automatically select any existing cross-section.

Refresh Tag Formats	Refreshes the tag formatting.

Run Batch 	Displays a dialog box showing the available Tag Data batches along with a description.  From this dialog, you choose which batch process to run.


Column(s) As Input

Description

Allows you to tag columns of data as Input.  If a column is not tagged, the data in that column will not be used in the creation of the data files.  To tag a column of data as Input, you must select the entire column (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Column(s) As Input sub-menu item.  Multiple (contiguous or non-contiguous) columns can be selected and tagged all at once.  The following text describes the coloring/formatting scheme for the Input tag.

Input Tag Coloring/Formatting

If no row tags exist, the cells for a column tagged as Input will be colored aqua and the data will be colored gray.  If any row tags exist, the cells (for the entire worksheet) will be colored white (the default color) and the color of the data (in the column tagged as Input) will depend upon the tag type for its corresponding row.


Column(s) As Desired

Description

Allows you to tag columns of data as Desired.  If a column is not tagged, the data in that column will not be used in the creation of the data files.  To tag a column of data as Desired, you must select the entire column (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Column(s) As Desired sub-menu item.  Multiple (contiguous or non-contiguous) columns can be selected and tagged all at once.  The following text describes the coloring/formatting scheme for the Desired tag.

Desired Tag Coloring/Formatting

If no row tags exist, the cells for a column tagged as Desired will be colored aqua and the data will be colored gray and formatted as bold italic.  If any row tags exist, the cells (for the entire worksheet) will be colored white (the default color), the color of the data (in the column tagged as Desired) will depend upon the tag type for its corresponding row, and the data (in the column tagged as Desired) will be formatted as bold italic.


Column(s) As Symbol

Description

Allows you to tag columns of data as Symbol.  To tag a column of data as Symbol, you must select the entire column (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Column(s) As Symbol sub-menu item.  Multiple (contiguous or non-contiguous) columns can be selected and tagged all at once.  When you tag a column as symbol, the “(S)” prefix will be appended to the beginning of that columns label.  Any column with this prefix will be recognized as a symbolic column by NeuroSolutions for Excel.  Columns tagged as Symbol are unary encoded when you click the Translate Symbolic Columns sub-menu item from the Preprocess Data menu item.

A Column should be tagged as symbol if either of the following two conditions exist:

1	The column contains non-numeric data.

2	The desired output column should be tagged as symbol if the problem being solved is a classification problem.  For the classification option of the Test procedure to function properly, the desired output must be unary encoded.


Row(s) As Training

Description

Allows you to tag rows of data as Training.  If a row is not tagged, the data in that row will not be used in the creation of the data files.  To tag a row of data as Training, you must select the entire row (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Row(s) As Training sub-menu item.  Multiple (contiguous or non-contiguous) rows can be selected and tagged all at once.  The following text describes the coloring/formatting scheme for the Training tag.

Training Tag Coloring/Formatting

If no column tags exist, the cells for a row tagged as Training will be colored black and the data will be colored gray.  If any column tags exist, the cells (for the entire worksheet) will be colored white (the default color), the data that exists within a tagged column will be colored black, and this data will be formatted based on the particular column tag.


Row(s) As Cross Validation

Description

Allows you to tag rows of data as Cross Validation.  If a row is not tagged, the data in that row will not be used in the creation of the data files.  To tag a row of data as Cross Validation, you must select the entire row (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Row(s) As Cross Validation sub-menu item.  Multiple (contiguous or non-contiguous) rows can be selected and tagged all at once.  The following text describes the coloring/formatting scheme for the Cross Validation tag.

Cross Validation Tag Coloring/Formatting

If no column tags exist, the cells for a row tagged as Cross Validation will be colored red and the data will be colored gray.  If any column tags exist, the cells (for the entire worksheet) will be colored white (the default color), the data that exists within a tagged column will be colored red, and this data will be formatted based on the particular column tag.


Row(s) As Testing

Description

Allows you to tag rows of data as Testing.  If a row is not tagged, the data in that row will not be used in the creation of the data files.  To tag a row of data as Testing, you must select the entire row (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Row(s) As Testing sub-menu item.  Multiple (contiguous or non-contiguous) rows can be selected and tagged all at once.  The following text describes the coloring/formatting scheme for the Testing tag.

Testing Tag Coloring/Formatting

If no column tags exist, the cells for a row tagged as Testing will be colored blue and the data will be colored gray.  If any column tags exist, the cells (for the entire worksheet) will be colored white (the default color), the data that exists within a tagged column will be colored blue, and this data will be formatted based on the particular column tag.


Row(s) As Production

Description

Allows you to tag rows of data as Production.  If a row is not tagged, the data in that row will not be used in the creation of the data files.  To tag a row of data as Production, you must select the entire row (see Selecting Columns and Rows) then select the Tag Data menu item followed by the Row(s) As Production sub-menu item.  Multiple (contiguous or non-contiguous) rows can be selected and tagged all at once.  The following text describes the coloring/formatting scheme for the Production tag.

Testing Tag Coloring/Formatting

If no column tags exist, the cells for a row tagged as Testing will be colored green and the data will be colored gray.  If any column tags exist, the cells (for the entire worksheet) will be colored white (the default color), the data that exists within a tagged column will be colored green, and this data will be formatted based on the particular column tag.


All Columns As Input

Description

Provides a quick method for tagging multiple columns of data.  Select this sub-menu item if you want to tag all of the columns in the active worksheet as Input.


All Non-Numeric Columns As Symbol

Description

Provides a quick method for tagging multiple columns of data.  Select this sub-menu item if you want to tag all of the non-numeric columns in the active worksheet as Symbol.  If a column of data contains any non-numeric values (except for labels), it will be tagged as Symbol.


All Rows As Training

Description

Provides a quick method for tagging multiple rows of data.  Select this sub-menu item if you want to tag all of the rows in the active worksheet as Training.


Rows By Percentages

Description

Provides a quick method for tagging multiple rows of data.  Select this sub-menu item if you want to tag user-specified percentages of your data as Training, Cross Validation, and Testing.  A dialog box will display allowing you to enter the percentages (of the total number of rows) to tag as Cross Validation and Testing.  The remainder of the data will be tagged as Training.

Dialog Box Options

Training

Displays the percentage of data that will be used for Training. This cell cannot be changed manually. It will automatically be updated based on changes in the Cross Validation and Testing percentages.

Cross Validation

Enter the percentage of data to use for Cross Validation.

Testing

Enter the percentage of data to use for Testing.

Reverse Tagging Order

By default, the data will be tagged in the following order: Training, Cross Validation, Testing. However, checking this option will cause the tagging order to be reversed such that the data is tagged in the following order: Testing, Cross Validation, Training.


Clear Tags

Description

Select this sub-menu item if you want to clear one or more of the available tags on the active worksheet.  This will display a dialog box showing all of the tags that exist on the active worksheet.  Select one or more of these tags then click OK to clear the tags you selected.

Dialog Box Options

Available Tags

Choose the tag or tags that you want to clear.  When you click OK, the selected tags will be cleared.


Clear Column Tag

Description

Select this sub-menu item if you want to clear the tag(s) for the selected column(s).  Multiple (contiguous or non-contiguous) columns can be selected and have their tags cleared all at once.  If multiple columns are selected, they may have different tag types (Input or Desired).


Clear Symbol Tag

Description

Select this sub-menu item if you want to clear the Symbol tag for the selected column(s).  Multiple (contiguous or non-contiguous) columns can be selected and have their Symbol tags cleared all at once.  This operation has no affect when performed on columns that are not tagged as symbol.


Clear Row Tag

Description

Select this sub-menu item if you want to clear the tag(s) for the selected row(s).  Multiple (contiguous or non-contiguous) rows can be selected and have their tags cleared all at once.  If multiple rows are selected, they may have different tag types (Training, Cross Validation, Testing, or Production).


Clear All Tags

Description

Select the sub-menu item if you want to clear all of the tags for the active worksheet.


Select Cross-Section

Description

Select this sub-menu item if you want to select one of the available cross-sections.  This will display a dialog box showing all of the available cross-sections.  Choose the cross-section that you want to select then click OK and the cross-section will be highlighted.  This gives you a visual indication of the different sections of data.  Furthermore, since the cross-section is automatically selected, you can easily cut or copy the data for a particular cross-section.

Dialog Box Options

Available Cross-Sections

Choose the cross-section that you want to select.  When you click OK, the chosen cross-section will be selected.


Refresh Tag Formats

Description

Refreshes the tag coloring after you have made changes to the spreadsheet such as inserting another column of data.  After selecting this menu item, the colors of the spreadsheet will again reflect what data is actually tagged as well as the tag type.


Run Batch (Tag Data)

Description

Selection of this menu item will display a dialog box listing all of the custom Tag Data batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Tag Data batches displayed, click on its name then click the OK button.

A Tag Data batch is identified by its Module name.  All modules beginning with the prefix “NSETag” are interpreted as Tag Data batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.


Create Data Files Module

Menu Item	Description

All Files	Creates data files for all tagged cross-sections within the active worksheet.

Training Files	Creates Training Input and Training Desired files from the correspondingly tagged cross-sections within the active worksheet.

Cross Validation Files	Creates Cross Validation Input and Cross Validation Desired files from the correspondingly tagged cross-sections within the active worksheet.

Testing Files	Creates Testing Input and Testing Desired files from the correspondingly tagged data cross-sections within the active worksheet.

Production Input File 	Creates Production Input file from the correspondingly tagged data cross-section within the active worksheet.

View Data File 	Allows you to view (in Notepad) a data file that was created for the active worksheet.

Delete Data Files	Deletes all of the files previously created for the active worksheet.

Run Batch 	Displays a dialog box showing the available Create Data Files batches along with a description.  From this dialog, you choose which batch process to run.


All Files

Description

When this menu item is selected, files for Training Input, Training Desired, Cross Validation Input, Cross Validation Desired, Testing Input, Testing Desired, and Production Input are created using the data on the active worksheet (provided that these cross-sections are tagged).  The files are named and located according to the following naming convention:

Path = [Workbook Path]

Name =	[Workbook Name (w/o extension)].[Worksheet Name].[Corresponding Suffix].asc

where [Corresponding Suffix] = “TrainingInput”, “TrainingDesired”, “CrossValidationInput”, “CrossValidationDesired”, “TestingInput”, “TestingDesired”, or “ProductionInput”


Training Files

Description

When this menu item is selected, files for Training Input and Training Desired are created using the data on the active worksheet (provided that these cross-sections are tagged).  The files are named and located according to the following naming convention:

Path = [Workbook Path]

Name =	[Workbook Name (w/o extension)].[Worksheet Name].[Corresponding Suffix].asc

where [Corresponding Suffix] = “TrainingInput” or “TrainingDesired”


Cross Validation Files

Description

When this menu item is selected, files for Cross Validation Input and Cross Validation Desired are created using the data on the active worksheet (provided that these cross-sections are tagged).  The files are named and located according to the following naming convention:

Path = [Workbook Path]

Name =	[Workbook Name (w/o extension)].[Worksheet Name].[Corresponding Suffix].asc

where [Corresponding Suffix] = “CrossValidationInput” or “CrossValidationDesired”


Testing Files

Description

When this menu item is selected, files for Testing Input and Testing Desired are created using the data on the active worksheet (provided that these cross-sections are tagged).  The files are named and located according to the following naming convention:

Path = [Workbook Path]

Name =	[Workbook Name (w/o extension)].[Worksheet Name].[Corresponding Suffix].asc

where [Corresponding Suffix] = “TestingInput” or “TestingDesired”


Production Input File

Description

When this menu item is selected, a file for the Production Input is created using the data on the active worksheet (provided that this cross-sections is tagged).  The files are named and located according to the following naming convention:

Path = [Workbook Path]

Name =	[Workbook Name (w/o extension)].[Worksheet Name].ProductionInput.asc


View Data File

Description

When this menu item is selected, a dialog box is displayed showing all of the files that were previously created for the active worksheet.  Chose the file you want to view then click OK.  The selected file will then be opened within Notepad.

Dialog Box Options

Available Data Files

Choose the file that you want to view.  When you click OK, the chosen file will be opened within Notepad.


Delete Data Files

Description

Select this menu item to permanently delete all of the data files that were previously created for the active worksheet.


Run Batch (Create Data Files)

Description

Selection of this menu item will display a dialog box listing all of the custom Create Data Files batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Create Data Files batches displayed, click on its name then click the OK button.

A Create Data Files batch is identified by its Module name.  All modules beginning with the prefix “NSECreateFiles” are interpreted as Create Data Files batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.


Create/Open Network Module

Menu Item	Description

New	Starts the NeuralBuilder which guides you step-by-step through the creation of a new NeuroSolutions breadboard.

Open	Opens an existing NeuroSolutions breadboard.

Close	Closes the active NeuroSolutions breadboard.

Save	Saves the active NeuroSolutions breadboard.

Save As	Allows you to save the active NeuroSolutions breadboard to a user-specified location.

Load Best Weights	Loads the best weights for the active network.

Tile Excel/NS	Horizontally tiles Microsoft Excel and NeuroSolutions.

Run Batch 	Displays a dialog box showing the available Create Network batches along with a description.  From this dialog, you choose which batch process to run.


New

Description

Selection of this menu item will open the NeuralBuilder.

The NeuralBuilder leads you through a series of panels that prompt you for information regarding the network you want to create.  The Build button on the last panel will open NeuroSolutions (if it’s not already open) and automatically create the network that you specified.

If the sheet that is active when you click the New menu item contains tagged data, the default network parameters (number of PEs and Step Sizes for each layer) will be chosen intelligently based on the number of columns tagged as Input, the number of columns tagged as Desired, and the number of rows tagged as Training.


Open

Description

Selection of this menu item will display the Open NeuroSolutions Breadboard dialog box.

Use this dialog box to transverse the file system on your computer and find the breadboard file that you want to open.  Double click this file or select it and click the Open button.  This will run NeuroSolutions (if it is not already running) and the selected breadboard will be opened within NeuroSolutions.  When you open a NeuroSolutions breadboard, a hidden sheet named NSVariables is created (if it does not already exist) within the active workbook.  This sheet keeps track of the active Breadboard Path and the Breadboard File Name.  If the NSVariables sheet already exists and another breadboard is already referenced within this sheet, you will be given the choice of whether or not to make the breadboard just opened the active breadboard.  If you choose to make it the active breadboard, the Breadboard Path and the Breadboard File Name will be updated to reference the breadboard just opened.


Close

Description

Selection of this menu item will close the breadboard currently active within NeuroSolutions.  You will be warned if the breadboard has changed since it was last saved or if has never been saved.


Save

Description

Selection of this menu item will save the breadboard currently active within NeuroSolutions.  If the Backup on Save switch is on (under View|Options|Save within NeuroSolutions), the previously saved version of the active NeuroSolutions breadboard will be renamed to its original name with “.back” concatenated onto the end.  The active NeuroSolutions breadboard is then saved to the file system using the original name.  This gives you a level of protection in case you want to go back to a previous version of the breadboard.  If a breadboard has not previously been saved, the Save As dialog box will be displayed.


Save As

Description

Selection of this menu item will display the Save NeuroSolutions Breadboard As dialog box.

Use this dialog box to transverse the file system on your computer to the location at which you want to save the breadboard currently active within NeuroSolutions.  Type the name for the breadboard into the File name edit box then click Save.  When you save a NeuroSolutions breadboard, a sheet named NSVariables is created (if it does not already exist) within the active workbook.  This sheet keeps track of the active Breadboard Path and the Breadboard File Name.  If the NSVariables sheet already exists and another breadboard is already referenced within this sheet, you will be given the choice of whether or not to make the breadboard just saved the active breadboard.  If you choose to make it the active breadboard, the Breadboard Path and the Breadboard File Name will be updated to reference the breadboard just saved.


Load Best Weights

Description

Selection of this menu item will load the best network weights (and parameters if the breadboard contains a genetic controller) saved during a training run.  Note that if a cross validation data set was used during training, the best network weights were automatically saved when the cross validation error was minimum.  If only a training data set was used during training, the best network weights were automatically saved when the training error was minimum.  The weights loaded correspond to the NeuroSolutions breadboard referenced within the project information sheet (NSVariables).  If this breadboard is not already open, it will be opened and the best network weights will be loaded into it.  If the weights for this breadboard cannot be found, this is reported to the user.


Tile Excel/NS

Description

Select this menu item to tile Excel and NeuroSolutions horizontally.  Your particular screen resolution will be read and the two applications will be tiled accordingly.  Tiling the two applications gives you the ability to see them both at the same time and easily switch between them.  Furthermore, you can view what is happening in NeuroSolutions (through the use of probes) while an Excel training or testing process is running.


Run Batch (Create Network)

Description

Selection of this menu item will display a dialog box listing all of the custom Create Network batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Create Network batches displayed, click on its name then click the OK button.

A Create Network batch is identified by its Module name.  All modules beginning with the prefix “NSECreateNet” are interpreted as Create Network batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.


Train Network Module

Menu Item	Description

Train	Trains the active NeuroSolutions breadboard one time and creates a report of the results.

Train N Times 	Trains the active NeuroSolutions breadboard N times with different random initial conditions and creates a report of the results.

Vary a Parameter 	Trains the active NeuroSolutions breadboard N times for each value of a network parameter as the parameter is varied from a user defined starting value by a user defined increment for a user defined number of variations.

Train Genetic 	Trains the active NeuroSolutions breadboard while genetically optimizing the choice of inputs and parameter values to achieve the best model.

Run Batch 	Displays a dialog box showing the available Train Network batches along with a description.  From this dialog, you choose which batch process to run.


Train

Description

The active NeuroSolutions breadboard is trained one time and the best network weights are saved (Note: The best network weights are saved at the epoch when the cross validation error is minimum if a cross validation data set is used or the epoch at which the training error is minimum if only a training data set is used).  A report of the training results is then generated.  The generated report contains the following information:

1	Plot of the training mean-squared error (MSE) versus Epochs.  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this plot will also show the cross validation MSE.

2	Table showing the minimum training MSE, the epoch at which this minimum training MSE occurred, and the final training MSE.  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this table will include the minimum cross validation MSE, the epoch at which this minimum cross validation error occurred, and the final cross validation MSE.

The following data sheets are also created:

Data Sheet Name	Description

TrialName MSE	Contains the training MSE for each epoch in the run (learning curve).  Also, the minimum training MSE is given at the bottom this column. If cross validation was used, the same results are also shown for the cross validation data set.

Dialog Box Options

Trial Name

Enter the name for this particular training trial run.  If the entered name has already been used, you will be given the option to overwrite it.

Number of Epochs

Enter the total number of epochs to train the network for.  An epoch is defined as one complete presentation of all of the data.  By default, this setting is initialized to the Epochs / Run setting of the active NeuroSolutions breadboard.

Use Cross Validation

Check this box if you want to use cross validation during training.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation.

Randomize Initial Weights

Check this box if you want to randomize the networks weights before training (this is the default).  Unchecking this box allows you to start from where a previous training run left off.

Cross Validation Termination

Check this box to enable the termination of the training run if the cross validation error has not improved within the user-specified number of epochs.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation or if the Use Cross Validation option is unchecked.

Make Classes Evenly Weighted

Check this box to tell NeuroSolutions to evenly weight each class in a classification problem.  This will enable the "Exemplar Weighting" feature of NeuroSolutions and will cause the gradients for each class to be weighted proportionately according to the number of samples of that class present in the training dataset.  For classification problems in which one or more classes are disproportionately represented, using this feature will usually help the network to produce a more balanced model. Specifically, the underrepresented classes will be given more weight, which will usually improve their classification.  For example, suppose you have a two-class problem where 99% of your samples are of the Class1 type and 1% are of the Class2 type.  Without exemplar weighting, the network will likely take the path of least resistance and arrive at a model that classifies all of your data as Class1.  Overall, the network will be 99% accurate, but this is probably not what you want since it will be 0% correct for Class2.  With exemplar weighting, the gradients (used for updating the weights) for Class2 samples will be weighted 99 times more than the gradients for Class1 samples.  This will usually result in a more correct classification percentage for Class2 samples.  For more information on "Exemplar Weighting", see the "Exemplar Weighting Inspector" help topic in the NeuroSolutions help.  Note: This feature should only be enabled for classification problems.


Train N Times

Description

Trains the active NeuroSolutions breadboard N times (where N is chosen by the user) with different random initial conditions and the best network weights are saved (Note: The best network weights are saved at the run and epoch when the cross validation error is minimum if a cross validation data set is used or the run and epoch at which the training error is minimum if only a training data set is used).  A report is then generated showing the results.  The generated report contains the following information:

1	Plot of the average training mean-squared error (MSE) versus Epochs (the average is computed by averaging the MSE of the N runs at each Epoch).  The high (average + 1 standard deviation) and low (average - 1 standard deviation) standard deviation boundaries are also shown on the plot using dashed lines of the same color as the average line (the standard deviation is computed across the N runs at each Epoch).  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this plot will also show the average cross validation MSE and the corresponding standard deviation boundaries.

2	Table showing the average of the minimum training MSEs and the average of the final training MSEs over the N runs along with their corresponding standard deviations.  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this table will include the average of the minimum cross validation MSEs and the average of the final cross validation MSEs over the N runs and their corresponding standard deviations.

3	Table reporting the best training MSE out of all N runs, the epoch and run # at which this best training MSE occurred, and the final training MSE for this best run.  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this table also presents the best cross validation MSE out of all N runs, the epoch and run # at which this best cross validation MSE occurred, and the final cross validation MSE for this best run.

4	Plot of the training MSE versus Epoch for each of the N runs.

5	Plot of the cross validation MSE versus Epoch for each of the N runs if the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below).

The following data sheets are also created:

Data Sheet Name	Description

TrialName TrnMSE	Contains the training MSEs for each run.  Also, the minimum training MSE for each run is given at the bottom of its corresponding column.

TrialName CVMSE	Contains the cross validation MSEs for each run.  Also, the minimum cross validation MSE for each run is given at the bottom of its corresponding column (this sheet is only created if cross validation is used).

TrialName AvgMSE	Contains the average training MSE, average cross validation MSE (if cross validation is used), and corresponding standard deviation boundaries.

Dialog Box Options

Trial Name

Enter the name for this particular training trial run.  If the entered name has already been used, you will be given the option to overwrite it.

Number of Epochs

Enter the total number of epochs to train the network for.  An epoch is defined as one complete presentation of all of the data.  By default, this setting is initialized to the Epochs / Run setting of the active NeuroSolutions breadboard.

Number of Runs

Enter the number of times you want to run this network.  Since each run begins with different random initial weights, the more times you run the network the more statistically sound your results will be.

Use Cross Validation

Check this box if you want to use cross validation during training.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation.

Cross Validation Termination

Check this box to enable the termination of a training run if the cross validation error has not improved within the user-specified number of epochs.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation or if the Use Cross Validation option is unchecked.

Make Classes Evenly Weighted

Check this box to tell NeuroSolutions to evenly weight each class in a classification problem.  This will enable the "Exemplar Weighting" feature of NeuroSolutions and will cause the gradients for each class to be weighted proportionately according to the number of samples of that class present in the training dataset.  For classification problems in which one or more classes are disproportionately represented, using this feature will usually help the network to produce a more balanced model. Specifically, the underrepresented classes will be given more weight, which will usually improve their classification.  For example, suppose you have a two-class problem where 99% of your samples are of the Class1 type and 1% are of the Class2 type.  Without exemplar weighting, the network will likely take the path of least resistance and arrive at a model that classifies all of your data as Class1.  Overall, the network will be 99% accurate, but this is probably not what you want since it will be 0% correct for Class2.  With exemplar weighting, the gradients (used for updating the weights) for Class2 samples will be weighted 99 times more than the gradients for Class1 samples.  This will usually result in a more correct classification percentage for Class2 samples.  For more information on "Exemplar Weighting", see the "Exemplar Weighting Inspector" help topic in the NeuroSolutions help.  Note: This feature should only be enabled for classification problems.


Vary a Parameter

Description

For this training process, the user picks a network parameter to vary.  This parameter can be selected from a default list or typed in.  Next the starting value of the chosen network parameter is entered along with the desired number of variations and the size of the increment between each variation.  When the user clicks OK, the active NeuroSolutions breadboard is trained N times (where N is also chosen by the user) for each value of the network parameter and the best network weights are saved (Note: The best network weights are saved at the parameter variation, run, and epoch when the cross validation error is minimum if a cross validation data set is used or the parameter variation, run, and epoch at which the training error is minimum if only a training data set is used).  After training, a report is automatically generated summarizing the results.  The generated report contains the following information:

1	Plot of the average of the minimum training mean-squared errors (MSEs) for each value of the varied parameter.  The high (average + 1 standard deviation) and low (average - 1 standard deviation) standard deviation boundaries are also shown on the plot using dashed lines of the same color as the average line.  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this plot will also show the average of the minimum cross validation MSEs for each value of the varied parameter and the corresponding standard deviation boundaries.

2	Table reporting the best training MSE out of all N runs and all values of the varied parameter, the parameter value, epoch, and run # at which this best training MSE occurred, and the final training MSE for this best run.  If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), this table also presents the best cross validation MSE out of all N runs and all values of the varied parameter, the parameter value, epoch, and run # at which this best cross validation MSE occurred, and the final cross validation MSE for this best run.

3	Plot of the average training MSE versus Epochs (the average is computed by averaging the MSE of the N runs at each Epoch).  This average is calculated and a curve is displayed for each value of the varied parameter.

4	If the active worksheet contains Cross Validation Input and Cross Validation Desired tags and the user checks Use Cross Validation (see below), a plot will be generated for the average cross validation MSE versus Epochs (the average is computed by averaging the MSE of the N runs at each Epoch).  This average is calculated and a curve is displayed for each value of the varied parameter.

The following data sheets are also created:

Data Sheet Name	Description

TrialName TrnMSE(v#)	Contains the training MSEs for all N runs.  Also, the minimum training MSE for each run is given at the bottom of its corresponding column.  The # sign shown at the left signifies the variation number.  Thus, a separate sheet is created for each parameter value.

TrialName CVMSE(v#)	Contains the cross validation MSEs for each run.  Also, the minimum cross validation MSE for each run is given at the bottom of its corresponding column (this sheet is only created if cross validation is used).  The # sign shown at the left signifies the variation number.  Thus, a separate sheet is created for each parameter value.

TrialName AvgMinMSE	Contains the average of the minimum MSEs calculated over N runs and the corresponding standard deviations.  These values are computed for each value of the varied parameter.  This data is also computed for the cross validation set if cross validation is used.

TrialName AvgTrnMSE	Contains the average training MSE (averaged over the N runs) for each epoch.  Each column contains this data for a different value of the varied parameter.

TrialName AvgCVMSE	if cross validation is used, the worksheet contains the average cross validation MSE (averaged over the N runs) for each epoch.  Each column contains this data for a different value of the varied parameter.

Dialog Box Options

Trial Name

Enter the name for this particular training trial run.  If the entered name has already been used, you will be given the option to overwrite it.

Number of Epochs

Enter the total number of epochs to train the network for.  An epoch is defined as one complete presentation of all of the data.  By default, this setting is initialized to the Epochs / Run setting of the active NeuroSolutions breadboard.

Number of Runs

Enter the number of times you want to run this network.  Since each run begins with different random initial weights, the more times you run the network the more statistically sound your results will be.

Use Cross Validation

Check this box if you want to use cross validation during training.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation.

Cross Validation Termination

Check this box to enable the termination of a training run if the cross validation error has not improved within the user-specified number of epochs.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation or if the Use Cross Validation option is unchecked.

Make Classes Evenly Weighted

Check this box to tell NeuroSolutions to evenly weight each class in a classification problem.  This will enable the "Exemplar Weighting" feature of NeuroSolutions and will cause the gradients for each class to be weighted proportionately according to the number of samples of that class present in the training dataset.  For classification problems in which one or more classes are disproportionately represented, using this feature will usually help the network to produce a more balanced model. Specifically, the underrepresented classes will be given more weight, which will usually improve their classification.  For example, suppose you have a two-class problem where 99% of your samples are of the Class1 type and 1% are of the Class2 type.  Without exemplar weighting, the network will likely take the path of least resistance and arrive at a model that classifies all of your data as Class1.  Overall, the network will be 99% accurate, but this is probably not what you want since it will be 0% correct for Class2.  With exemplar weighting, the gradients (used for updating the weights) for Class2 samples will be weighted 99 times more than the gradients for Class1 samples.  This will usually result in a more correct classification percentage for Class2 samples.  For more information on "Exemplar Weighting", see the "Exemplar Weighting Inspector" help topic in the NeuroSolutions help.  Note: This feature should only be enabled for classification problems.

Component.Action

Use the combo box to select the parameter that you want to vary or manually enter a custom one into the box.  The parameter is chosen using the form Component.Action where Component is the name of the NeuroSolutions component which contains the parameter to be varied (Note: this name is always shown on the Engine page of a components inspector) and Action is the action that you want to perform on the chosen component.  For example, hiddenAxon1.setRows would be chosen if the parameter you want to vary is the number of rows (processing elements) in the first hidden layer.  Note: If the word “selection“ is used for the component name, the action is performed on the currently selected component(s) on the NeuroSolutions breadboard.  This is useful if you want to vary the same parameter on multiple components at the same time.  For example, to vary the step size for all of the Momentum components on a breadboard at once, you would select all of the Momentum components (by holding down the Shift key and clicking on each Momentum component one at a time) and use selection.setStepSize as the Component.Action.

Start Value

Enter the starting value for the parameter that you are varying.

Increment

Enter the value to increment the parameter by at each step.

# of Variations

Enter the total number of parameter variations desired.  Note: The starting value counts as a parameter variation.  For example, if Start Value = 2, Increment = 1, and # of Variations = 4, the Vary A Parameter routine will use the following 4 parameter values: 2, 3, 4, and 5.

Descriptive Name

A default name is automatically assigned for the name of the parameter being varied (based on the selected Component.Action).  This is the name that will be used in the report.  The user can change this name if desired.  If the user types in a custom Component.Action, the name for this parameter must be entered in this cell.


Train Genetic

Description

Trains the active NeuroSolutions breadboard while genetically optimizing the networks choice of inputs, step sizes, momentum values, and the number of processing elements in the hidden layer(s) (Note: Other parameters can also be optimized by setting them up manually within NeuroSolutions). The goal of the optimization is to find the parameter settings that result in the minimum error. If cross validation is used, the goal will be to minimize the cross validation error.  Otherwise, the goal will be to minimize the training error.  The best weights and parameter settings will automatically be saved during training and can be loaded into the NeuroSolutions breadboard by clicking the Load Best Weights menu item.  However, this is not normally necessary since the best weights and parameters will automatically be loaded during testing (if the Load Best option is selected – the default) or during the application of the Production dataset.

To perform genetic training, first an initial population of networks is randomly created with each having a different set of parameters.  Each of these networks is then trained and evaluated (to determine its fitness) based on the minimum error it achieved.  The characteristics of the good networks are then combined and mutated to create a new population of networks.  Again, the networks in this population are evaluated and the characteristics of the best networks are passed along to the next generation of networks.  This process is repeated until the Maximum Generations or Maximum Evolution Time is reached or the user stops the evolution.

After training, a report is automatically generated summarizing the results.  The generated report contains the following information:

1	Plot of the best fitness achieved during each generation of the optimization.  The best fitness is the overall minimum MSE (cross validation MSE if cross validation was used or the training MSE otherwise) among all of the networks within the corresponding generation.

2	Plot of the average fitness achieved during each generation of the optimization.  The average fitness is the average of the minimum MSE (cross validation MSE if cross validation was used or the training MSE otherwise) taken across all of the networks within the corresponding generation.

3	Table summarizing the best fitness and the average fitness plots.  For each of these plots, the minimum MSE (across all generations), the generation of this minimum, and the final MSE are displayed.

Note: If the user stops the genetic training before one generation has been completed, the report will not show any results.

The following data sheet is also created:

Data Sheet Name	Description

TrialName Fitness	Contains the best fitness and the average fitness for each generation.  Also, the minimum of the best fitness and the minimum of the average fitness are given at the bottom of its corresponding column.

Dialog Box Options

Trial Name

Enter the name for this particular training trial run.  If the entered name has already been used, you will be given the option to overwrite it.

Number of Epochs

Enter the total number of epochs to train the network for.  An epoch is defined as one complete presentation of all of the data.  By default, this setting is initialized to the Epochs / Run setting of the active NeuroSolutions breadboard.

Population Size

Enter the number of networks to create for each population.  Increasing the population size increases the coverage of the parameter settings within a population, but if it is increased too much, it will reduce overall efficiency due to replication.  By default, this setting is initialized to the Population Size setting of the active NeuroSolutions breadboard.

Maximum Generations

Enter the maximum number of generations to evolve for.  A generation is defined as one complete evaluation of a population (the training and evaluation of all of the networks in the population).  By default, this setting is initialized to the Maximum Generations setting of the active NeuroSolutions breadboard.

Maximum Evolution Time

Enter the maximum number of minutes to evolve for.  By default, this setting is initialized to the Elapsed Time Termination setting of the active NeuroSolutions breadboard.

Use Cross Validation

Check this box if you want to use cross validation during training.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation.

Cross Validation Termination

Check this box to enable the termination of a training run if the cross validation error has not improved within the user-specified number of epochs.  Note: You will not be able to check this box if the active worksheet does not contain data tagged as Cross Validation or if the Use Cross Validation option is unchecked.

Make Classes Evenly Weighted

Check this box to tell NeuroSolutions to evenly weight each class in a classification problem.  This will enable the "Exemplar Weighting" feature of NeuroSolutions and will cause the gradients for each class to be weighted proportionately according to the number of samples of that class present in the training dataset.  For classification problems in which one or more classes are disproportionately represented, using this feature will usually help the network to produce a more balanced model. Specifically, the underrepresented classes will be given more weight, which will usually improve their classification.  For example, suppose you have a two-class problem where 99% of your samples are of the Class1 type and 1% are of the Class2 type.  Without exemplar weighting, the network will likely take the path of least resistance and arrive at a model that classifies all of your data as Class1.  Overall, the network will be 99% accurate, but this is probably not what you want since it will be 0% correct for Class2.  With exemplar weighting, the gradients (used for updating the weights) for Class2 samples will be weighted 99 times more than the gradients for Class1 samples.  This will usually result in a more correct classification percentage for Class2 samples.  For more information on "Exemplar Weighting", see the "Exemplar Weighting Inspector" help topic in the NeuroSolutions help.  Note: This feature should only be enabled for classification problems.

Input Optimization

Check the inputs that you want to genetically optimize (allow the genetic algorithm to turn on or off).  Uncheck the inputs that you want to always be on.

Step Size Optimization

Check the Enable box if you want to genetically optimize the network’s step sizes. This will enable the optimization of the step sizes for all Gradient components in NeuroSolutions.  If you enable this optimization, you can use the Lower Bound and Upper Bound text boxes to specify the range that the optimized parameter(s) should stay within.

Momentum Optimization

Check the Enable box if you want to genetically optimize the network’s momentum values. This will enable the optimization of the momentum values for all Gradient components in NeuroSolutions that use momentum.  If you enable this optimization, you can use the Lower Bound and Upper Bound text boxes to specify the range that the optimized parameter(s) should stay within.

Processing Element Optimization

Check the Enable box if you want to genetically optimize the number of processing elements in all of the network’s hidden layers. This will enable the optimization of the rows parameter for each of the axons whose Component Name starts with “hidden” (see the Engine tab of the component’s inspector).  If you enable this optimization, you can use the Lower Bound and Upper Bound text boxes to specify the range that the optimized parameter(s) should stay within.

Advanced Genetic Options

Click this button to set up the advanced genetic options.  The following dialog will be displayed:

For information on the advanced genetic settings, please download the Genetic Server Help from our web site and read the chapter on Genetic Algorithm Theory.


Run Batch (Train Network)

Description

Selection of this menu item will display a dialog box listing all of the custom Train Network batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Train Network batches displayed, click on its name then click the OK button.

A Train Network batch is identified by its Module name.  All modules beginning with the prefix “NSETrain” are interpreted as Train Network batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.

The Code Workbook contains a custom Train Network batch named Train Test that is provided for demonstration purposes.  Below is a picture of the Run Train Network Batch dialog and a short description of this batch.

Dialog Box Options

Batch Name

Lists all of the Train Network batches available.  Select the one that you want to run then click OK to run it.

Batch Description

Displays the description for the currently selected Train Network batch.

Custom Train Network Batches

Batch Name	Description

Train Test 	Trains the active NeuroSolutions breadboard one time then tests the performance using the Training data.


Train Test

Description

This custom Train Network batch runs the NeuroSolutions for Excel Train process followed by the NeuroSolutions for Excel Test process.  The Train and Test dialog boxes are not displayed.  Instead, the parameters for these dialogs are defined within the Train Test code in the Code Workbook (the NSETrainTest module in NSECodeWorkbook.xls).  See the code to view or modify the settings.  The main purpose of this batch is to demonstrate how to call built-in NeuroSolutions for Excel functions from custom batches.


Test Network Module

Menu Item	Description

Test	Tests the active NeuroSolutions breadboard on the chosen data set and creates a report of the results.

Sensitivity About the Mean	Performs sensitivity analysis on the chosen data set.

Run Batch 	Displays a dialog box showing the available Test Network batches along with a description.  From this dialog, you choose which batch process to run.


Test

Description

Allows the user to test a network on the chosen data set (Training, Cross Validation, or Testing).  The user also has the option to use the current network weights or use the best network weights (Note: If a cross validation set is used during training, the best network weights are the ones that give the minimum cross validation error.  Otherwise, the best network weights are the ones that give the minimum training error) saved during a training trial run (Note: if you choose to load the best network weights, the active breadboard must have the same topology as the one for which the best weights were created).  During testing, the learning is turned off and the chosen data set is fed through the network.  The network output is collected and a report is then generated showing the testing results.  The contents of this generated report varies based on whether the Classification or Regression report type was selected.  The generated report contains the following information:

1	Regression selected:  Plot of the network output and the desired network output for each output.  Each desired output will be plotted as a solid color and the corresponding network output will be a dashed line of the same color.

2	Classification selected:  Confusion matrix showing the number of outputs classified as members of each class.

3	Table reporting the mean-squared error (MSE), normalized mean-squared error (NMSE), mean absolute error (MAE), minimum absolute error, maximum absolute error, and correlation coefficient (r) for each output.  If the Classification report type was selected, this table also includes the percent correct for each class.

The following data sheet is also created:

Data Sheet Name	Description

TrialName IOData	The testing input(s) and desired output(s) used are copied to this sheet.  After the testing data has been run through the network, the network output is also written to this worksheet.

Dialog Box Options

Trial Name

Enter the name for this particular testing trial run.  If the entered name has already been used, you will be given the option to overwrite it.

Data Set to Test

Select the data set that you want to test.  Only the data sets available for testing are shown.

Weights

1	Load Best: Choose this option to load the best network weights (saved during a training trial run) before the network is tested.

2	Use Current: Choose this option to test using the current network weights.

Report Type

1	Classification:  Choose this option if you are trying to solve a classification problem.  Note: If you choose this option, the desired output should be unary encoded.  See the help for the Column(s) As Symbol sub-menu item of the Tag Data sub-menu.

2	 Regression:  Choose this option if the problem being solved is not a classification problem.


Sensitivity About the Mean

Description

This testing process provides a measure of the relative importance among the inputs of the neural model and illustrates how the model output varies in response to variation of an input.  The first input is varied between its mean +/- a user-defined number of standard deviations while all other inputs are fixed at their respective means. The network output is computed for a user-defined number of steps above and below the mean.  This process is repeated for each input.  A report is generated which summarizes the variation of each output with respect to the variation in each input. The generated report contains the following information:

1	A 3D Column plot of the table in 2.

2	Table reporting the standard deviation of each output divided by the standard deviation of the input which was varied to create the output.

3	A plot is created for each input showing the network output(s) over the range of the varied input.

The following data sheet(s) is/are also created:

Data Sheet Name	Description

TrialName Output(#)	A new worksheet is created for each varied input.  The varied input values are written to the worksheet along with the corresponding network output.  The # sign shown at the left signifies the number of the varied input.

Dialog Box Options

Trial Name

Enter the name for this particular testing trial run.  If the entered name has already been used, you will be given the option to overwrite it.

Data Set to Test

Select the data set that you want to test.  Only the data sets available for testing are shown.

Weights

1	Load Best: Choose this option to load the best network weights (saved during a training trial run) before the network is tested.

2	Use Current: Choose this option to test using the current network weights.

Standard Deviations

Enter the number of standard deviations to add and subtract from the mean of an input when calculating the range over which the input is varied.

Steps per Side

Enter the number of steps to use on each side of the mean. This is the number of discrete values that will be used to calculate the output.


Run Batch (Test Network)

Description

Selection of this menu item will display a dialog box listing all of the custom Test Network batches available within the Code Workbook (NSECodeWorkbook.xls) and within the active workbook.  This dialog box displays the batch name and the batch description for each of the batches found.  To run one of the Test Network batches displayed, click on its name then click the OK button.

A Test Network batch is identified by its Module name.  All modules beginning with the prefix “NSETest” are interpreted as Test Network batches and will be shown within the dialog.  If a module’s name starts with this prefix, it must follow the custom batch protocol.  For more information on this protocol and on creating custom batches see the help topic for the New Batch menu item.


Apply Production Dataset

Description

Select this menu item to apply your neural model to the data tagged as Production.  This will feed the Production Input data into the neural model and produce the model output.  The output will be written back to your data sheet at the location of the Production Desired cross-section.  Note that by definition, Production data does not have a desired output since Production data is data that is used for the application of the neural model. Thus, the Production Desired cross-section will be empty until the Production dataset has been applied.


New Batch

Description

Select this menu item to create a new custom batch process.  A dialog box will be displayed that will allow you to choose the type of custom batch that you want to create.  There are 7 types of custom batches that are recognized by NeuroSolutions for Excel.  These 7 types are listed below along with their required Module Name Prefixes:

Batch Type	Module Prefix

Preprocess Data	NSEPreprocess

Analyze Data	NSEAnalyze

Tag Data	NSETag

Create Network	NSECreateNet

Create Data Files	NSECreateFiles

Train Network	NSETrain

Test Network	NSETest

Select the type of batch you want to create, enter a name and a description for the batch, then click OK.  A blank custom batch template module of the proper batch type will be copied to the active workbook. This batch template contains the following 3 procedures:

Function OnGetName() As String

OnGetName = "Your Batch Name"

End Function

Function OnGetDescription() As String

OnGetDescription = "Your Batch Description"

End Function

Sub OnRunBatchProcess()

'Enter the code for your custom <Batch Type> batch here

MsgBox "Custom batch process has not been implemented.", vbInformation + vbOKOnly

End Sub

where <Batch Type> is one of the 7 batch types discussed above.

The OnGetName() and OnGetDescription() functions will contain the batch name and description that you entered into the New Batch dialog.  This name and description will appear in the Run Batch dialog when you click the Run Batch menu item for the same batch type.  To make changes to the batch name or description, simply edit these functions.

You will need to write the code to execute when this custom batch is run.  This code should be inserted into the OnRunBatchProcess() subroutine.  Note: The names of these three procedures cannot be modified.

Dialog Box Options

Batch Type

Choose the type of batch that you want to create.

Module Prefix

Shows the Module Prefix for the chosen batch type.  This field is not editable.

Batch Name

Enter a name for the custom batch.  This batch name is used to name the corresponding module (see Module Name below) and for display in the Run Batch dialog.

Module Name

This field shows the module name of the new batch.  It is simply the concatenation of the Module Prefix with the Batch Name. This field is not editable.

Batch Name

Enter a description for the custom batch.  The batch description is used for display in the Run Batch dialog.


Batch Manager

Description

Selection of this menu item will display the batch manager dialog box for the chosen batch type.   This dialog lists all of the batches found (for the chosen batch type) within the active workbook and within the code workbook (NSECodeWorkbook.xls).  This dialog provides an easy interface for editing/deleting custom batches and for moving them between the active workbook and the code workbook.  A batch process stored within the active workbook will only be available when that workbook is loaded and is the active workbook.  If you want a custom batch process to always be available when NeuroSolutions for Excel is loaded, you should store it within the code workbook (simply use the Batch Manager to move it from the active workbook to the code workbook).

Note:  The delete and move operations of the Batch Manager will be performed on all of the Excel objects whose name matches any of the following templates.  This allows you to use Modules, Class Modules, Forms, Worksheets, and Charts when creating a custom batch.

• Modules:	[ModuleName]
• Class Modules:	[ModuleName + “Class*”]
• Forms:	[ModuleName + “Form*”] or [ModuleName + “Dialog*”]
• Worksheets:	[ModuleName + “WSheet*”]
• Charts:	[ModuleName + “Chart*”]

where

* = wildcard that can represent any number of characters.

+ = concatenation

Dialog Box Options

Active Workbook Batches

List of the batches available within the active workbook for the selected batch type.

Batch Type

Change the batch type to show the batches available for other batch types.

Code Workbook Batches

List of the batches available within the code workbook for the selected batch type.


Goto Active Data Sheet

Description

Selection of this menu item will take you to the last data sheet for which a training or testing process was run.  If no training or testing processes have been run, a dialog box will be displayed listing all of the tagged data sheets within the active workbook (see Data Sheets).  If this dialog box is displayed, select the data sheet you want to goto then click OK.


Data Sheets

Description

Selection of this menu item will display a dialog box listing all of the tagged data sheets within the active workbook.  Select the data sheet you want to goto then click OK.

Dialog Box Options

Available Data Sheets

Lists all of the tagged data sheets within the active workbook.  Select the one you want to activate then click OK.


Goto Active Report

Description

Selection of this menu item will take you to the last report generated by a training or testing process (the active report).  The name of the active report is stored on the hidden project information sheet (NSVariables).  If the stored active report name is blank or the NSVariables has not been created yet, the Reports dialog box is displayed listing all of the available reports within the active workbook.  If this dialog box is displayed, select the report you want to view then click Goto.  If there are no reports in the active workbook, a message is displayed conveying this information.


Reports

Description

Selection of this menu item will display all of the reports available within the active workbook. Select the report you want to view then click Goto.  Reports can also be deleted from this dialog.  To delete a report, select the report that you want to delete then click Delete.  This will permanently delete all of the sheets that were created for the chosen trial (i.e. the report and all of the associated data sheets).

Dialog Box Options

Available Reports

Lists all of the reports available within the active workbook.  Select the one you want to goto or delete then click Goto or Delete, respectively.


Open Active Network

Description

Selection of this menu item will open the active neural network.  The path and name of the active network is stored in the hidden project information sheet (NSVariables) for a particular project workbook.  When this menu item is selected, the appropriate NeuroSolutions breadboard is opened and Excel and NeuroSolutions are automatically tiled.