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Archive-name: neural-net-faq
Last-modified: 1995/04/20
URL: http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html
Maintainer: prechelt@ira.uka.de (Lutz Prechelt)
------------------------------------------------------------------------
Additions, corrections, or improvements are always welcome.
Anybody who is willing to contribute any information,
please email me; if it is relevant, I will incorporate it.
The monthly posting departs at the 28th of every month.
------------------------------------------------------------------------
This is a monthly posting to the Usenet newsgroup comp.ai.neural-nets
(and comp.answers, where it should be findable at ANY time). Its
purpose is to provide basic information for individuals who are new to the
field of neural networks or are just beginning to read this group. It shall
help to avoid lengthy discussion of questions that usually arise for
beginners of one or the other kind.
SO, PLEASE, SEARCH THIS POSTING FIRST IF YOU HAVE A QUESTION
and
DON'T POST ANSWERS TO FAQs: POINT THE ASKER TO THIS POSTING
This posting is archived in the periodic posting archive on host
rtfm.mit.edu (and on some other hosts as well). Look in the anonymous
ftp directory "/pub/usenet/news.answers", the filename is as given in the
'Archive-name:' header above. If you do not have anonymous ftp access,
you can access the archives by mail server as well. Send an E-mail
message to mail-server@rtfm.mit.edu with "help" and "index" in the
body on separate lines for more information.
For those of you who read this posting anywhere other than in
comp.ai.neural-nets: To read comp.ai.neural-nets (or post articles to it)
you need Usenet News access. Try the commands, 'xrn', 'rn', 'nn', or 'trn'
on your Unix machine, 'news' on your VMS machine, or ask a local
guru.
This monthly posting is also available as a hypertext document in WWW
(World Wide Web) under the URL
"http://wwwipd.ira.uka.de/~prechelt/FAQ/neural-net-faq.html"
The monthly posting is not meant to discuss any topic exhaustively.
Disclaimer:
This posting is provided 'as is'.
No warranty whatsoever is expressed or implied,
in particular, no warranty that the information contained herein
is correct or useful in any way, although both is intended.
To find the answer of question number 'x', search for the string
"x. A:" (so the answer to question 12 is at 12. A: )
And now, in the end, we begin:
========== Questions ==========
********************************
1. What is this newsgroup for? How shall it be used?
2. What is a neural network (NN)?
3. What can you do with a Neural Network and what not?
4. Who is concerned with Neural Networks?
5. What does 'backprop' mean? What is 'overfitting'?
6. Why use a bias input? Why activation functions?
7. How many hidden units should I use?
8. How many learning methods for NNs exist? Which?
9. What about Genetic Algorithms?
10. What about Fuzzy Logic?
11. How are NNs related to statistical methods?
12. Good introductory literature about Neural Networks?
13. Any journals and magazines about Neural Networks?
14. The most important conferences concerned with Neural
Networks?
15. Neural Network Associations?
16. Other sources of information about NNs?
17. Freely available software packages for NN simulation?
18. Commercial software packages for NN simulation?
19. Neural Network hardware?
20. Databases for experimentation with NNs?
========== Answers ==========
******************************
1. A: What is this newsgroup for? How shall it be
=================================================
used?
=====
The newsgroup comp.ai.neural-nets is intended as a forum for
people who want to use or explore the capabilities of Artificial
Neural Networks or Neural-Network-like structures.
There should be the following types of articles in this newsgroup:
1. Requests
+++++++++++
Requests are articles of the form "I am looking for
X" where X is something public like a book, an article, a
piece of software. The most important about such a request
is to be as specific as possible!
If multiple different answers can be expected, the person
making the request should prepare to make a summary of
the answers he/she got and announce to do so with a
phrase like "Please reply by email, I'll
summarize to the group" at the end of the posting.
The Subject line of the posting should then be something
like "Request: X"
2. Questions
++++++++++++
As opposed to requests, questions ask for a larger piece of
information or a more or less detailed explanation of
something. To avoid lots of redundant traffic it is important
that the poster provides with the question all information
s/he already has about the subject asked and state the
actual question as precise and narrow as possible. The
poster should prepare to make a summary of the answers
s/he got and announce to do so with a phrase like
"Please reply by email, I'll summarize to
the group" at the end of the posting.
The Subject line of the posting should be something like
"Question: this-and-that" or have the form of a
question (i.e., end with a question mark)
3. Answers
++++++++++
These are reactions to questions or requests. As a rule of
thumb articles of type "answer" should be rare. Ideally, in
most cases either the answer is too specific to be of general
interest (and should thus be e-mailed to the poster) or a
summary was announced with the question or request (and
answers should thus be e-mailed to the poster).
The subject lines of answers are automatically adjusted by
the news software. Note that sometimes longer threads of
discussion evolve from an answer to a question or request.
In this case posters should change the subject line suitably
as soon as the topic goes too far away from the one
announced in the original subject line. You can still carry
along the old subject in parentheses in the form
"Subject: new subject (was: old subject)"
4. Summaries
++++++++++++
In all cases of requests or questions the answers for which
can be assumed to be of some general interest, the poster of
the request or question shall summarize the answers he/she
received. Such a summary should be announced in the
original posting of the question or request with a phrase
like "Please answer by email, I'll
summarize"
In such a case, people who answer to a question should
NOT post their answer to the newsgroup but instead mail
them to the poster of the question who collects and reviews
them. After about 5 to 20 days after the original posting, its
poster should make the summary of answers and post it to
the newsgroup.
Some care should be invested into a summary:
o simple concatenation of all the answers is not
enough: instead, redundancies, irrelevancies,
verbosities, and errors should be filtered out (as good
as possible)
o the answers should be separated clearly
o the contributors of the individual answers should be
identifiable (unless they requested to remain
anonymous [yes, that happens])
o the summary should start with the "quintessence" of
the answers, as seen by the original poster
o A summary should, when posted, clearly be
indicated to be one by giving it a Subject line
starting with "SUMMARY:"
Note that a good summary is pure gold for the rest of the
newsgroup community, so summary work will be most
appreciated by all of us. Good summaries are more valuable
than any moderator ! :-)
5. Announcements
++++++++++++++++
Some articles never need any public reaction. These are
called announcements (for instance for a workshop,
conference or the availability of some technical report or
software system).
Announcements should be clearly indicated to be such by
giving them a subject line of the form "Announcement:
this-and-that"
6. Reports
++++++++++
Sometimes people spontaneously want to report something
to the newsgroup. This might be special experiences with
some software, results of own experiments or conceptual
work, or especially interesting information from
somewhere else.
Reports should be clearly indicated to be such by giving
them a subject line of the form "Report:
this-and-that"
7. Discussions
++++++++++++++
An especially valuable possibility of Usenet is of course
that of discussing a certain topic with hundreds of potential
participants. All traffic in the newsgroup that can not be
subsumed under one of the above categories should belong
to a discussion.
If somebody explicitly wants to start a discussion, he/she
can do so by giving the posting a subject line of the form
"Subject: Discussion: this-and-that"
It is quite difficult to keep a discussion from drifting into
chaos, but, unfortunately, as many many other newsgroups
show there seems to be no secure way to avoid this. On the
other hand, comp.ai.neural-nets has not had many
problems with this effect in the past, so let's just go and
hope...
------------------------------------------------------------------------
2. A: What is a neural network (NN)?
====================================
First of all, when we are talking about a neural network, we
*should* usually better say "artificial neural network" (ANN),
because that is what we mean most of the time. Biological neural
networks are much more complicated in their elementary
structures than the mathematical models we use for ANNs.
A vague description is as follows:
An ANN is a network of many very simple processors ("units"),
each possibly having a (small amount of) local memory. The units
are connected by unidirectional communication channels
("connections"), which carry numeric (as opposed to symbolic)
data. The units operate only on their local data and on the inputs
they receive via the connections.
The design motivation is what distinguishes neural networks from
other mathematical techniques:
A neural network is a processing device, either an algorithm, or
actual hardware, whose design was motivated by the design and
functioning of human brains and components thereof.
Most neural networks have some sort of "training" rule whereby
the weights of connections are adjusted on the basis of presented
patterns. In other words, neural networks "learn" from examples,
just like children learn to recognize dogs from examples of dogs,
and exhibit some structural capability for generalization.
Neural networks normally have great potential for parallelism,
since the computations of the components are independent of each
other.
------------------------------------------------------------------------
3. A: What can you do with a Neural Network and
===============================================
what not?
=========
In principle, NNs can compute any computable function, i.e. they
can do everything a normal digital computer can do. Especially
anything that can be represented as a mapping between vector
spaces can be approximated to arbitrary precision by feedforward
NNs (which is the most often used type).
In practice, NNs are especially useful for mapping problems which
are tolerant of some errors, have lots of example data available,
but to which hard and fast rules can not easily be applied. NNs
are, at least today, difficult to apply successfully to problems that
concern manipulation of symbols and memory.
------------------------------------------------------------------------
4. A: Who is concerned with Neural Networks?
============================================
Neural Networks are interesting for quite a lot of very dissimilar
people:
o Computer scientists want to find out about the properties
of non-symbolic information processing with neural nets
and about learning systems in general.
o Engineers of many kinds want to exploit the capabilities of
neural networks on many areas (e.g. signal processing) to
solve their application problems.
o Cognitive scientists view neural networks as a possible
apparatus to describe models of thinking and conscience
(High-level brain function).
o Neuro-physiologists use neural networks to describe and
explore medium-level brain function (e.g. memory, sensory
system, motorics).
o Physicists use neural networks to model phenomena in
statistical mechanics and for a lot of other tasks.
o Biologists use Neural Networks to interpret nucleotide
sequences.
o Philosophers and some other people may also be interested
in Neural Networks for various reasons.
------------------------------------------------------------------------
5. A: What does 'backprop' mean? What is
========================================
'overfitting'?
===============
'Backprop' is an abbreviation for 'backpropagation of error' which
is the most widely used learning method for neural networks
today. Although it has many disadvantages, which could be
summarized in the sentence "You are almost not knowing what
you are actually doing when using backpropagation" :-) it has
pretty much success on practical applications and is relatively easy
to apply.
It is for the training of layered (i.e., nodes are grouped in layers)
feedforward (i.e., the arcs joining nodes are unidirectional, and
there are no cycles) nets (often called "multi layer perceptrons").
Back-propagation needs a teacher that knows the correct output
for any input ("supervised learning") and uses gradient descent on
the error (as provided by the teacher) to train the weights. The
activation function is (usually) a sigmoidal (i.e., bounded above
and below, but differentiable) function of a weighted sum of the
nodes inputs.
The use of a gradient descent algorithm to train its weights makes
it slow to train; but being a feedforward algorithm, it is quite rapid
during the recall phase.
Literature:
Rumelhart, D. E. and McClelland, J. L. (1986): Parallel
Distributed Processing: Explorations in the Microstructure
of Cognition (volume 1, pp 318-362). The MIT Press.
(this is the classic one) or one of the dozens of other books or
articles on backpropagation (see also answer "books").
'Overfitting' (often also called 'overtraining' or 'overlearning') is
the phenomenon that in most cases a network gets worse instead
of better after a certain point during training when it is trained to
as low errors as possible. This is because such long training may
make the network 'memorize' the training patterns, including all
of their peculiarities. However, one is usually interested in the
generalization of the network, i.e., the error it exhibits on examples
NOT seen during training. Learning the peculiarities of the
training set makes the generalization worse. The network should
only learn the general structure of the examples.
There are various methods to fight overfitting. The two most
important classes of such methods are regularization methods
(such as weight decay) and early stopping. Regularization
methods try to limit the complexity of the network such that it is
unable to learn peculiarities. Early stopping aims at stopping the
training at the point of optimal generalization. A description of the
early stopping method can for instance be found in section 3.3 of
/pub/papers/techreports/1994-21.ps.Z on ftp.ira.uka.de
(anonymous ftp).
------------------------------------------------------------------------
6. A: Why use a bias input? Why activation
==========================================
functions?
===========
One way of looking at the need for bias inputs is that the inputs to
each unit in the net define an N-dimensional space, and the unit
draws a hyperplane through that space, producing an "on" output
on one side and an "off" output on the other. (With sigmoid units
the plane will not be sharp -- there will be some gray area of
intermediate values near the separating plane -- but ignore this
for now.)
The weights determine where this hyperplane is in the input space.
Without a bias input, this separating plane is constrained to pass
through the origin of the hyperspace defined by the inputs. For
some problems that's OK, but in many problems the plane would
be much more useful somewhere else. If you have many units in a
layer, they share the same input space and without bias would
ALL be constrained to pass through the origin.
Activation functions are needed to introduce nonlinearity into the
network. Without nonlinearity, hidden units would not make nets
more powerful than just plain perceptrons (which do not have any
hidden units, just input and output units). The reason is that a
composition of linear functions is again a linear function.
However, it is just the nonlinearity (i.e, the capability to represent
nonlinear functions) that makes multilayer networks so powerful.
Almost any nonlinear function does the job, although for
backpropagation learning it must be differentiable and it helps if
the function is bounded; the popular sigmoidal functions and
gaussian functions are the most common choices.
------------------------------------------------------------------------
7. A: How many hidden units should I use?
==========================================
There is no way to determine a good network topology just from
the number of inputs and outputs. It depends critically on the
number of training examples and the complexity of the
classification you are trying to learn. There are problems with one
input and one output that require millions of hidden units, and
problems with a million inputs and a million outputs that require
only one hidden unit, or none at all.
Some books and articles offer "rules of thumb" for choosing a
topopology -- Ninputs plus Noutputs dividied by two, maybe with
a square root in there somewhere -- but such rules are total
garbage. Other rules relate to the number of examples available:
Use at most so many hidden units that the number of weights in
the network times 10 is smaller than the number of examples.
Such rules are only concerned with overfitting and are unreliable
as well.
------------------------------------------------------------------------
8. A: How many learning methods for NNs exist?
==============================================
Which?
======
There are many many learning methods for NNs by now. Nobody
knows exactly how many. New ones (at least variations of existing
ones) are invented every week. Below is a collection of some of the
most well known methods; not claiming to be complete.
The main categorization of these methods is the distinction of
supervised from unsupervised learning:
In supervised learning, there is a "teacher" who in the learning
phase "tells" the net how well it performs ("reinforcement
learning") or what the correct behavior would have been ("fully
supervised learning").
In unsupervised learning the net is autonomous: it just looks at the
data it is presented with, finds out about some of the properties of
the data set and learns to reflect these properties in its output.
What exactly these properties are, that the network can learn to
recognise, depends on the particular network model and learning
method.
Many of these learning methods are closely connected with a
certain (class of) network topology.
Now here is the list, just giving some names:
1. UNSUPERVISED LEARNING (i.e. without a "teacher"):
1). Feedback Nets:
a). Additive Grossberg (AG)
b). Shunting Grossberg (SG)
c). Binary Adaptive Resonance Theory (ART1)
d). Analog Adaptive Resonance Theory (ART2, ART2a)
e). Discrete Hopfield (DH)
f). Continuous Hopfield (CH)
g). Discrete Bidirectional Associative Memory (BAM)
h). Temporal Associative Memory (TAM)
i). Adaptive Bidirectional Associative Memory (ABAM)
j). Kohonen Self-organizing Map/Topology-preserving map (SOM/TPM)
k). Competitive learning
2). Feedforward-only Nets:
a). Learning Matrix (LM)
b). Driver-Reinforcement Learning (DR)
c). Linear Associative Memory (LAM)
d). Optimal Linear Associative Memory (OLAM)
e). Sparse Distributed Associative Memory (SDM)
f). Fuzzy Associative Memory (FAM)
g). Counterprogation (CPN)
2. SUPERVISED LEARNING (i.e. with a "teacher"):
1). Feedback Nets:
a). Brain-State-in-a-Box (BSB)
b). Fuzzy Congitive Map (FCM)
c). Boltzmann Machine (BM)
d). Mean Field Annealing (MFT)
e). Recurrent Cascade Correlation (RCC)
f). Learning Vector Quantization (LVQ)
g). Backpropagation through time (BPTT)
h). Real-time recurrent learning (RTRL)
i). Recurrent Extended Kalman Filter (EKF)
2). Feedforward-only Nets:
a). Perceptron
b). Adaline, Madaline
c). Backpropagation (BP)
d). Cauchy Machine (CM)
e). Adaptive Heuristic Critic (AHC)
f). Time Delay Neural Network (TDNN)
g). Associative Reward Penalty (ARP)
h). Avalanche Matched Filter (AMF)
i). Backpercolation (Perc)
j). Artmap
k). Adaptive Logic Network (ALN)
l). Cascade Correlation (CasCor)
m). Extended Kalman Filter(EKF)
------------------------------------------------------------------------
9. A: What about Genetic Algorithms?
====================================
There are a number of definitions of GA (Genetic Algorithm). A
possible one is
A GA is an optimization program
that starts with
a population of encoded procedures, (Creation of Life :-> )
mutates them stochastically, (Get cancer or so :-> )
and uses a selection process (Darwinism)
to prefer the mutants with high fitness
and perhaps a recombination process (Make babies :-> )
to combine properties of (preferably) the succesful mutants.
Genetic Algorithms are just a special case of the more general idea
of ``evolutionary computation''. There is a newsgroup that is
dedicated to the field of evolutionary computation called
comp.ai.genetic. It has a detailed FAQ posting which, for instance,
explains the terms "Genetic Algorithm", "Evolutionary
Programming", "Evolution Strategy", "Classifier System", and
"Genetic Programming". That FAQ also contains lots of pointers
to relevant literature, software, other sources of information, et
cetera et cetera. Please see the comp.ai.genetic FAQ for further
information.
------------------------------------------------------------------------
10. A: What about Fuzzy Logic?
==============================
Fuzzy Logic is an area of research based on the work of L.A.
Zadeh. It is a departure from classical two-valued sets and logic,
that uses "soft" linguistic (e.g. large, hot, tall) system variables and
a continuous range of truth values in the interval [0,1], rather
than strict binary (True or False) decisions and assignments.
Fuzzy logic is used where a system is difficult to model exactly
(but an inexact model is available), is controlled by a human
operator or expert, or where ambiguity or vagueness is common. A
typical fuzzy system consists of a rule base, membership functions,
and an inference procedure.
Most Fuzzy Logic discussion takes place in the newsgroup
comp.ai.fuzzy, but there is also some work (and discussion) about
combining fuzzy logic with Neural Network approaches in
comp.ai.neural-nets.
For more details see (for example):
Klir, G.J. and Folger, T.A.: Fuzzy Sets, Uncertainty, and
Information Prentice-Hall, Englewood Cliffs, N.J., 1988.
Kosko, B.: Neural Networks and Fuzzy Systems Prentice Hall,
Englewood Cliffs, NJ, 1992.
------------------------------------------------------------------------
11. A: How are NNs related to statistical methods?
===================================================
There is considerable overlap between the fields of neural
networks and statistics.
Statistics is concerned with data analysis. In neural network
terminology, statistical inference means learning to generalize
from noisy data. Some neural networks are not concerned with
data analysis (e.g., those intended to model biological systems) and
therefore have little to do with statistics. Some neural networks do
not learn (e.g., Hopfield nets) and therefore have little to do with
statistics. Some neural networks can learn successfully only from
noise-free data (e.g., ART or the perceptron rule) and therefore
would not be considered statistical methods. But most neural
networks that can learn to generalize effectively from noisy data
are similar or identical to statistical methods. For example:
o Feedforward nets with no hidden layer (including
functional-link neural nets and higher-order neural nets)
are basically generalized linear models.
o Feedforward nets with one hidden layer are closely related
to projection pursuit regression.
o Probabilistic neural nets are identical to kernel
discriminant analysis.
o Kohonen nets for adaptive vector quantization are very
similar to k-means cluster analysis.
o Hebbian learning is closely related to principal component
analysis.
Some neural network areas that appear to have no close relatives
in the existing statistical literature are:
o Kohonen's self-organizing maps.
o Reinforcement learning ((although this is treated in the
operations research literature as Markov decision
processes).
o Stopped training (the purpose and effect of stopped training
are similar to shrinkage estimation, but the method is quite
different).
Feedforward nets are a subset of the class of nonlinear regression
and discrimination models. Statisticians have studied the
properties of this general class but had not considered the specific
case of feedforward neural nets before such networks were
popularized in the neural network field. Still, many results from
the statistical theory of nonlinear models apply directly to
feedforward nets, and the methods that are commonly used for
fitting nonlinear models, such as various Levenberg-Marquardt
and conjugate gradient algorithms, can be used to train
feedforward nets.
While neural nets are often defined in terms of their algorithms or
implementations, statistical methods are usually defined in terms
of their results. The arithmetic mean, for example, can be
computed by a (very simple) backprop net, by applying the usual
formula SUM(x_i)/n, or by various other methods. What you get
is still an arithmetic mean regardless of how you compute it. So a
statistician would consider standard backprop, Quickprop, and
Levenberg-Marquardt as different algorithms for implementing
the same statistical model such as a feedforward net. On the other
hand, different training criteria, such as least squares and cross
entropy, are viewed by statisticians as fundamentally different
estimation methods with different statistical properties.
It is sometimes claimed that neural networks, unlike statistical
models, require no distributional assumptions. In fact, neural
networks involve exactly the same sort of distributional
assumptions as statistical models, but statisticians study the
consequences and importance of these assumptions while most
neural networkers ignore them. For example, least-squares
training methods are widely used by statisticians and neural
networkers. Statisticians realize that least-squares training
involves implicit distributional assumptions in that least-squares
estimates have certain optimality properties for noise that is
normally distributed with equal variance for all training cases and
that is independent between different cases. These optimality
properties are consequences of the fact that least-squares
estimation is maximum likelihood under those conditions.
Similarly, cross-entropy is maximum likelihood for noise with a
Bernoulli distribution. If you study the distributional assumptions,
then you can recognize and deal with violations of the
assumptions. For example, if you have normally distributed noise
but some training cases have greater noise variance than others,
then you may be able to use weighted least squares instead of
ordinary least squares to obtain more efficient estimates.
Here are a few references:
Chatfield, C. (1993), "Neural networks: Forecasting breakthrough
or passing fad", International Journal of Forecasting, 9, 1-3.
Cheng, B. and Titterington, D.M. (1994), "Neural Networks: A
Review from a Statistical Perspective", Statistical Science, 9,
2-54.
Geman, S., Bienenstock, E. and Doursat, R. (1992), "Neural
Networks and the Bias/Variance Dilemma", Neural Computation,
4, 1-58.
Kushner, H. & Clark, D. (1978), _Stochastic Approximation
Methods for Constrained and Unconstrained Systems_,
Springer-Verlag.
Michie, D., Spiegelhalter, D.J. and Taylor, C.C. (1994), _Machine
Learning, Neural and Statistical Classification_, Ellis Horwood.
Ripley, B.D. (1993), "Statistical Aspects of Neural Networks", in
O.E. Barndorff-Nielsen, J.L. Jensen and W.S. Kendall, eds.,
_Networks and Chaos: Statistical and Probabilistic Aspects_,
Chapman & Hall. ISBN 0 412 46530 2.
Sarle, W.S. (1994), "Neural Networks and Statistical Models,"
Proceedings of the Nineteenth Annual SAS Users Group
International Conference, Cary, NC: SAS Institute, pp 1538-1550.
( ftp://ftp.sas.com/pub/sugi19/neural/neural1.ps)
White, H. (1989), "Learning in Artificial Neural Networks: A
Statistical Perspective," Neural Computation, 1, 425-464.
White, H. (1992), _Artificial Neural Networks: Approximation
and Learning Theory_, Blackwell.
------------------------------------------------------------------------
12. A: Good introductory literature about Neural
================================================
Networks?
=========
0.) The best (subjectively, of course -- please don't flame me):
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Haykin, S. (1994). Neural Networks, a Comprehensive
Foundation. Macmillan, New York, NY. "A very readable, well
written intermediate to advanced text on NNs Perspective is
primarily one of pattern recognition, estimation and signal
processing. However, there are well-written chapters on
neurodynamics and VLSI implementation. Though there is
emphasis on formal mathematical models of NNs as universal
approximators, statistical estimators, etc., there are also examples
of NNs used in practical applications. The problem sets at the end
of each chapter nicely complement the material. In the
bibliography are over 1000 references. If one buys only one book
on neural networks, this should be it."
Hertz, J., Krogh, A., and Palmer, R. (1991). Introduction to the
Theory of Neural Computation. Addison-Wesley: Redwood City,
California. ISBN 0-201-50395-6 (hardbound) and
0-201-51560-1 (paperbound) Comments: "My first impression is
that this one is by far the best book on the topic. And it's below
$30 for the paperback."; "Well written, theoretical (but not
overwhelming)"; It provides a good balance of model development,
computational algorithms, and applications. The mathematical
derivations are especially well done"; "Nice mathematical analysis
on the mechanism of different learning algorithms"; "It is NOT
for mathematical beginner. If you don't have a good grasp of
higher level math, this book can be really tough to get through."
Masters,Timothy (1994). Practical Neural Network Recipes in
C++. Academic Press, ISBN 0-12-479040-2, US $45 incl. disks.
"Lots of very good practical advice which most other books lack."
1.) Books for the beginner:
+++++++++++++++++++++++++++
Aleksander, I. and Morton, H. (1990). An Introduction to Neural
Computing. Chapman and Hall. (ISBN 0-412-37780-2).
Comments: "This book seems to be intended for the first year of
university education."
Beale, R. and Jackson, T. (1990). Neural Computing, an
Introduction. Adam Hilger, IOP Publishing Ltd : Bristol. (ISBN
0-85274-262-2). Comments: "It's clearly written. Lots of hints as
to how to get the adaptive models covered to work (not always
well explained in the original sources). Consistent mathematical
terminology. Covers perceptrons, error-backpropagation, Kohonen
self-org model, Hopfield type models, ART, and associative
memories."
Dayhoff, J. E. (1990). Neural Network Architectures: An
Introduction. Van Nostrand Reinhold: New York. Comments:
"Like Wasserman's book, Dayhoff's book is also very easy to
understand".
Fausett, L. V. (1994). Fundamentals of Neural Networks:
Architectures, Algorithms and Applications, Prentice Hall, ISBN
0-13-334186-0. Also published as a Prentice Hall International
Edition, ISBN 0-13-042250-9. Sample softeware (source code
listings in C and Fortran) is included in an Instructor's Manual.
"Intermediate in level between Wasserman and
Hertz/Krogh/Palmer. Algorithms for a broad range of neural
networks, including a chapter on Adaptive Resonace Theory with
ART2. Simple examples for each network."
Freeman, James (1994). Simulating Neural Networks with
Mathematica, Addison-Wesley, ISBN: 0-201-56629-X. Helps
the reader make his own NNs. The mathematica code for the
programs in the book is also available through the internet: Send
mail to MathSource@wri.com or try http://www.wri.com/ on the
World Wide Web.
Hecht-Nielsen, R. (1990). Neurocomputing. Addison Wesley.
Comments: "A good book", "comprises a nice historical overview
and a chapter about NN hardware. Well structured prose. Makes
important concepts clear."
McClelland, J. L. and Rumelhart, D. E. (1988). Explorations in
Parallel Distributed Processing: Computational Models of
Cognition and Perception (software manual). The MIT Press.
Comments: "Written in a tutorial style, and includes 2 diskettes of
NN simulation programs that can be compiled on MS-DOS or
Unix (and they do too !)"; "The programs are pretty reasonable as
an introduction to some of the things that NNs can do."; "There
are *two* editions of this book. One comes with disks for the IBM
PC, the other comes with disks for the Macintosh".
McCord Nelson, M. and Illingworth, W.T. (1990). A Practical
Guide to Neural Nets. Addison-Wesley Publishing Company, Inc.
(ISBN 0-201-52376-0). Comments: "No formulas at all"; "It
does not have much detailed model development (very few
equations), but it does present many areas of application. It
includes a chapter on current areas of research. A variety of
commercial applications is discussed in chapter 1. It also includes a
program diskette with a fancy graphical interface (unlike the PDP
diskette)".
Muller, B. and Reinhardt, J. (1990). Neural Networks, An
Introduction. Springer-Verlag: Berlin Heidelberg New York
(ISBN: 3-540-52380-4 and 0-387-52380-4). Comments: The
book was developed out of a course on neural-network models
with computer demonstrations that was taught by the authors to
Physics students. The book comes together with a PC-diskette.
The book is divided into three parts: (1) Models of Neural
Networks; describing several architectures and learing rules,
including the mathematics. (2) Statistical Physiscs of Neural
Networks; "hard-core" physics section developing formal theories
of stochastic neural networks. (3) Computer Codes; explanation
about the demonstration programs. First part gives a nice
introduction into neural networks together with the formulas.
Together with the demonstration programs a 'feel' for neural
networks can be developed.
Orchard, G.A. & Phillips, W.A. (1991). Neural Computation: A
Beginner's Guide. Lawrence Earlbaum Associates: London.
Comments: "Short user-friendly introduction to the area, with a
non-technical flavour. Apparently accompanies a software
package, but I haven't seen that yet".
Rao, V.B & H.V. (1993). C++ Neural Networks and Fuzzy Logic.
MIS:Press, ISBN 1-55828-298-x, US $45 incl. disks. "Probably
not 'leading edge' stuff but detailed enough to get your hands
dirty!"
Wasserman, P. D. (1989). Neural Computing: Theory & Practice.
Van Nostrand Reinhold: New York. (ISBN 0-442-20743-3)
Comments: "Wasserman flatly enumerates some common
architectures from an engineer's perspective ('how it works')
without ever addressing the underlying fundamentals ('why it
works') - important basic concepts such as clustering, principal
components or gradient descent are not treated. It's also full of
errors, and unhelpful diagrams drawn with what appears to be
PCB board layout software from the '70s. For anyone who wants
to do active research in the field I consider it quite inadequate";
"Okay, but too shallow"; "Quite easy to understand"; "The best
bedtime reading for Neural Networks. I have given this book to
numerous collegues who want to know NN basics, but who never
plan to implement anything. An excellent book to give your
manager."
Wasserman, P.D. (1993). Advanced Methods in Neural
Computing. Van Nostrand Reinhold: New York (ISBN:
0-442-00461-3). Comments: Several neural network topics are
discussed e.g. Probalistic Neural Networks, Backpropagation and
beyond, neural control, Radial Basis Function Networks, Neural
Engineering. Furthermore, several subjects related to neural
networks are mentioned e.g. genetic algorithms, fuzzy logic, chaos.
Just the functionality of these subjects is described; enough to get
you started. Lots of references are given to more elaborate
descriptions. Easy to read, no extensive mathematical background
necessary.
2.) The classics:
+++++++++++++++++
Kohonen, T. (1984). Self-organization and Associative Memory.
Springer-Verlag: New York. (2nd Edition: 1988; 3rd edition:
1989). Comments: "The section on Pattern mathematics is
excellent."
Rumelhart, D. E. and McClelland, J. L. (1986). Parallel
Distributed Processing: Explorations in the Microstructure of
Cognition (volumes 1 & 2). The MIT Press. Comments: "As a
computer scientist I found the two Rumelhart and McClelland
books really heavy going and definitely not the sort of thing to
read if you are a beginner."; "It's quite readable, and affordable
(about $65 for both volumes)."; "THE Connectionist bible".
3.) Introductory journal articles:
++++++++++++++++++++++++++++++++++
Hinton, G. E. (1989). Connectionist learning procedures. Artificial
Intelligence, Vol. 40, pp. 185--234. Comments: "One of the better
neural networks overview papers, although the distinction
between network topology and learning algorithm is not always
very clear. Could very well be used as an introduction to neural
networks."
Knight, K. (1990). Connectionist, Ideas and Algorithms.
Communications of the ACM. November 1990. Vol.33 nr.11, pp
59-74. Comments:"A good article, while it is for most people easy
to find a copy of this journal."
Kohonen, T. (1988). An Introduction to Neural Computing.
Neural Networks, vol. 1, no. 1. pp. 3-16. Comments: "A general
review".
4.) Not-quite-so-introductory literature:
+++++++++++++++++++++++++++++++++++++++++
Anderson, J. A. and Rosenfeld, E. (Eds). (1988). Neurocomputing:
Foundations of Research. The MIT Press: Cambridge, MA.
Comments: "An expensive book, but excellent for reference. It is a
collection of reprints of most of the major papers in the field."
Anderson, J. A., Pellionisz, A. and Rosenfeld, E. (Eds). (1990).
Neurocomputing 2: Directions for Research. The MIT Press:
Cambridge, MA. Comments: "The sequel to their well-known
Neurocomputing book."
Caudill, M. and Butler, C. (1990). Naturally Intelligent Systems.
MIT Press: Cambridge, Massachusetts. (ISBN 0-262-03156-6).
Comments: "I guess one of the best books I read"; "May not be
suited for people who want to do some research in the area".
Cichocki, A. and Unbehauen, R. (1994). Neural Networks for
Optimization and Signal Processing. John Wiley & Sons, West
Sussex, England, 1993, ISBN 0-471-930105 (hardbound), 526
pages, $57.95. "Partly a textbook and partly a research
monograph; introduces the basic concepts, techniques, and models
related to neural networks and optimization, excluding rigorous
mathematical details. Accessible to a wide readership with a
differential calculus background. The main coverage of the book is
on recurrent neural networks with continuous state variables. The
book title would be more appropriate without mentioning signal
processing. Well edited, good illustrations."
Khanna, T. (1990). Foundations of Neural Networks.
Addison-Wesley: New York. Comments: "Not so bad (with a
page of erroneous formulas (if I remember well), and #hidden
layers isn't well described)."; "Khanna's intention in writing his
book with math analysis should be commended but he made
several mistakes in the math part".
Kung, S.Y. (1993). Digital Neural Networks, Prentice Hall,
Englewood Cliffs, NJ.
Levine, D. S. (1990). Introduction to Neural and Cognitive
Modeling. Lawrence Erlbaum: Hillsdale, N.J. Comments: "Highly
recommended".
Lippmann, R. P. (April 1987). An introduction to computing with
neural nets. IEEE Acoustics, Speech, and Signal Processing
Magazine. vol. 2, no. 4, pp 4-22. Comments: "Much acclaimed as
an overview of neural networks, but rather inaccurate on several
points. The categorization into binary and continuous- valued
input neural networks is rather arbitrary, and may work confusing
for the unexperienced reader. Not all networks discussed are of
equal importance."
Maren, A., Harston, C. and Pap, R., (1990). Handbook of Neural
Computing Applications. Academic Press. ISBN: 0-12-471260-6.
(451 pages) Comments: "They cover a broad area"; "Introductory
with suggested applications implementation".
Pao, Y. H. (1989). Adaptive Pattern Recognition and Neural
Networks Addison-Wesley Publishing Company, Inc. (ISBN
0-201-12584-6) Comments: "An excellent book that ties together
classical approaches to pattern recognition with Neural Nets. Most
other NN books do not even mention conventional approaches."
Rumelhart, D. E., Hinton, G. E. and Williams, R. J. (1986).
Learning representations by back-propagating errors. Nature, vol
323 (9 October), pp. 533-536. Comments: "Gives a very good
potted explanation of backprop NN's. It gives sufficient detail to
write your own NN simulation."
Simpson, P. K. (1990). Artificial Neural Systems: Foundations,
Paradigms, Applications and Implementations. Pergamon Press:
New York. Comments: "Contains a very useful 37 page
bibliography. A large number of paradigms are presented. On the
negative side the book is very shallow. Best used as a complement
to other books".
Zeidenberg. M. (1990). Neural Networks in Artificial Intelligence.
Ellis Horwood, Ltd., Chichester. Comments: "Gives the AI point
of view".
Zornetzer, S. F., Davis, J. L. and Lau, C. (1990). An Introduction
to Neural and Electronic Networks. Academic Press. (ISBN
0-12-781881-2) Comments: "Covers quite a broad range of
topics (collection of articles/papers )."; "Provides a primer-like
introduction and overview for a broad audience, and employs a
strong interdisciplinary emphasis".
------------------------------------------------------------------------
13. A: Any journals and magazines about Neural
==============================================
Networks?
=========
[to be added: comments on speed of reviewing and publishing,
whether they accept TeX format or ASCII by e-mail, etc.]
A. Dedicated Neural Network Journals:
+++++++++++++++++++++++++++++++++++++
Title: Neural Networks
Publish: Pergamon Press
Address: Pergamon Journals Inc., Fairview Park, Elmsford,
New York 10523, USA and Pergamon Journals Ltd.
Headington Hill Hall, Oxford OX3, 0BW, England
Freq.: 10 issues/year (vol. 1 in 1988)
Cost/Yr: Free with INNS or JNNS or ENNS membership ($45?),
Individual $65, Institution $175
ISSN #: 0893-6080
Remark: Official Journal of International Neural Network Society (INNS),
European Neural Network Society (ENNS) and Japanese Neural
Network Society (JNNS).
Contains Original Contributions, Invited Review Articles, Letters
to Editor, Book Reviews, Editorials, Announcements, Software Surveys.
Title: Neural Computation
Publish: MIT Press
Address: MIT Press Journals, 55 Hayward Street Cambridge,
MA 02142-9949, USA, Phone: (617) 253-2889
Freq.: Quarterly (vol. 1 in 1989)
Cost/Yr: Individual $45, Institution $90, Students $35; Add $9 Outside USA
ISSN #: 0899-7667
Remark: Combination of Reviews (10,000 words), Views (4,000 words)
and Letters (2,000 words). I have found this journal to be of
outstanding quality.
(Note: Remarks supplied by Mike Plonski "plonski@aero.org")
Title: IEEE Transactions on Neural Networks
Publish: Institute of Electrical and Electronics Engineers (IEEE)
Address: IEEE Service Cemter, 445 Hoes Lane, P.O. Box 1331, Piscataway, NJ,
08855-1331 USA. Tel: (201) 981-0060
Cost/Yr: $10 for Members belonging to participating IEEE societies
Freq.: Quarterly (vol. 1 in March 1990)
Remark: Devoted to the science and technology of neural networks
which disclose significant technical knowledge, exploratory
developments and applications of neural networks from biology to
software to hardware. Emphasis is on artificial neural networks.
Specific aspects include self organizing systems, neurobiological
connections, network dynamics and architecture, speech recognition,
electronic and photonic implementation, robotics and controls.
Includes Letters concerning new research results.
(Note: Remarks are from journal announcement)
Title: International Journal of Neural Systems
Publish: World Scientific Publishing
Address: USA: World Scientific Publishing Co., 1060 Main Street, River Edge,
NJ 07666. Tel: (201) 487 9655; Europe: World Scientific Publishing
Co. Ltd., 57 Shelton Street, London WC2H 9HE, England.
Tel: (0171) 836 0888; Asia: World Scientific Publishing Co. Pte. Ltd.,
1022 Hougang Avenue 1 #05-3520, Singapore 1953, Rep. of Singapore
Tel: 382 5663.
Freq.: Quarterly (Vol. 1 in 1990)
Cost/Yr: Individual $122, Institution $255 (plus $15-$25 for postage)
ISSN #: 0129-0657 (IJNS)
Remark: The International Journal of Neural Systems is a quarterly
journal which covers information processing in natural
and artificial neural systems. Contributions include research papers,
reviews, and Letters to the Editor - communications under 3,000
words in length, which are published within six months of receipt.
Other contributions are typically published within nine months.
The journal presents a fresh undogmatic attitude towards this
multidisciplinary field and aims to be a forum for novel ideas and
improved understanding of collective and cooperative phenomena with
computational capabilities.
Papers should be submitted to World Scientific's UK office. Once a
paper is accepted for publication, authors are invited to e-mail
the LaTeX source file of their paper in order to expedite publication.
Title: International Journal of Neurocomputing
Publish: Elsevier Science Publishers, Journal Dept.; PO Box 211;
1000 AE Amsterdam, The Netherlands
Freq.: Quarterly (vol. 1 in 1989)
Editor: V.D. Sanchez A.; German Aerospace Research Establishment;
Institute for Robotics and System Dynamics, 82230 Wessling, Germany.
Current events and software news editor: Dr. F. Murtagh, ESA,
Karl-Schwarzschild Strasse 2, D-85748, Garching, Germany,
phone +49-89-32006298, fax +49-89-32006480, email fmurtagh@eso.org
Title: Neural Processing Letters
Publish: D facto publications
Address: 45 rue Masui; B-1210 Brussels, Belgium
Phone: (32) 2 245 43 63; Fax: (32) 2 245 46 94
Freq: 6 issues/year (vol. 1 in September 1994)
Cost/Yr: BEF 4400 (about $140)
ISSN #: 1370-4621
Remark: The aim of the journal is to rapidly publish new ideas, original
developments and work in progress. Neural Processing Letters
covers all aspects of the Artificial Neural Networks field.
Publication delay is about 3 months.
FTP server available:
ftp://ftp.dice.ucl.ac.be/pub/neural-nets/NPL.
WWW server available:
http://www.dice.ucl.ac.be/neural-nets/NPL/NPL.html
Title: Neural Network News
Publish: AIWeek Inc.
Address: Neural Network News, 2555 Cumberland Parkway, Suite 299,
Atlanta, GA 30339 USA. Tel: (404) 434-2187
Freq.: Monthly (beginning September 1989)
Cost/Yr: USA and Canada $249, Elsewhere $299
Remark: Commercial Newsletter
Title: Network: Computation in Neural Systems
Publish: IOP Publishing Ltd
Address: Europe: IOP Publishing Ltd, Techno House, Redcliffe Way, Bristol
BS1 6NX, UK; IN USA: American Institute of Physics, Subscriber
Services 500 Sunnyside Blvd., Woodbury, NY 11797-2999
Freq.: Quarterly (1st issue 1990)
Cost/Yr: USA: $180, Europe: 110 pounds
Remark: Description: "a forum for integrating theoretical and experimental
findings across relevant interdisciplinary boundaries." Contents:
Submitted articles reviewed by two technical referees paper's
interdisciplinary format and accessability." Also Viewpoints and
Reviews commissioned by the editors, abstracts (with reviews) of
articles published in other journals, and book reviews.
Comment: While the price discourages me (my comments are based
upon a free sample copy), I think that the journal succeeds
very well. The highest density of interesting articles I
have found in any journal.
(Note: Remarks supplied by kehoe@csufres.CSUFresno.EDU)
Title: Connection Science: Journal of Neural Computing,
Artificial Intelligence and Cognitive Research
Publish: Carfax Publishing
Address: Europe: Carfax Publishing Company, P. O. Box 25, Abingdon,
Oxfordshire OX14 3UE, UK. USA: Carafax Publishing Company,
85 Ash Street, Hopkinton, MA 01748
Freq.: Quarterly (vol. 1 in 1989)
Cost/Yr: Individual $82, Institution $184, Institution (U.K.) 74 pounds
Title: International Journal of Neural Networks
Publish: Learned Information
Freq.: Quarterly (vol. 1 in 1989)
Cost/Yr: 90 pounds
ISSN #: 0954-9889
Remark: The journal contains articles, a conference report (at least the
issue I have), news and a calendar.
(Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl")
Title: Sixth Generation Systems (formerly Neurocomputers)
Publish: Gallifrey Publishing
Address: Gallifrey Publishing, PO Box 155, Vicksburg, Michigan, 49097, USA
Tel: (616) 649-3772, 649-3592 fax
Freq. Monthly (1st issue January, 1987)
ISSN #: 0893-1585
Editor: Derek F. Stubbs
Cost/Yr: $79 (USA, Canada), US$95 (elsewhere)
Remark: Runs eight to 16 pages monthly. In 1995 will go to floppy disc-based
publishing with databases +, "the equivalent to 50 pages per issue are
planned." Often focuses on specific topics: e.g., August, 1994 contains two
articles: "Economics, Times Series and the Market," and "Finite Particle
Analysis - [part] II." Stubbs also directs the company Advanced Forecasting
Technologies. (Remark by Ed Rosenfeld: ier@aol.com)
Title: JNNS Newsletter (Newsletter of the Japan Neural Network Society)
Publish: The Japan Neural Network Society
Freq.: Quarterly (vol. 1 in 1989)
Remark: (IN JAPANESE LANGUAGE) Official Newsletter of the Japan Neural
Network Society(JNNS)
(Note: remarks by Osamu Saito "saito@nttica.NTT.JP")
Title: Neural Networks Today
Remark: I found this title in a bulletin board of october last year.
It was a message of Tim Pattison, timpatt@augean.OZ
(Note: remark provided by J.R.M. Smits "anjos@sci.kun.nl")
Title: Computer Simulations in Brain Science
Title: Internation Journal of Neuroscience
Title: Neural Network Computation
Remark: Possibly the same as "Neural Computation"
Title: Neural Computing and Applications
Freq.: Quarterly
Publish: Springer Verlag
Cost/yr: 120 Pounds
Remark: Is the journal of the Neural Computing Applications Forum.
Publishes original research and other information
in the field of practical applications of neural computing.
B. NN Related Journals:
+++++++++++++++++++++++
Title: Complex Systems
Publish: Complex Systems Publications
Address: Complex Systems Publications, Inc., P.O. Box 6149, Champaign,
IL 61821-8149, USA
Freq.: 6 times per year (1st volume is 1987)
ISSN #: 0891-2513
Cost/Yr: Individual $75, Institution $225
Remark: Journal COMPLEX SYSTEMS devotes to rapid publication of research
on science, mathematics, and engineering of systems with simple
components but complex overall behavior. Send mail to
"jcs@complex.ccsr.uiuc.edu" for additional info.
(Remark is from announcement on Net)
Title: Biological Cybernetics (Kybernetik)
Publish: Springer Verlag
Remark: Monthly (vol. 1 in 1961)
Title: Various IEEE Transactions and Magazines
Publish: IEEE
Remark: Primarily see IEEE Trans. on System, Man and Cybernetics;
Various Special Issues: April 1990 IEEE Control Systems
Magazine.; May 1989 IEEE Trans. Circuits and Systems.;
July 1988 IEEE Trans. Acoust. Speech Signal Process.
Title: The Journal of Experimental and Theoretical Artificial Intelligence
Publish: Taylor & Francis, Ltd.
Address: London, New York, Philadelphia
Freq.: ? (1st issue Jan 1989)
Remark: For submission information, please contact either of the editors:
Eric Dietrich Chris Fields
PACSS - Department of Philosophy Box 30001/3CRL
SUNY Binghamton New Mexico State University
Binghamton, NY 13901 Las Cruces, NM 88003-0001
dietrich@bingvaxu.cc.binghamton.edu cfields@nmsu.edu
Title: The Behavioral and Brain Sciences
Publish: Cambridge University Press
Remark: (Expensive as hell, I'm sure.)
This is a delightful journal that encourages discussion on a
variety of controversial topics. I have especially enjoyed
reading some papers in there by Dana Ballard and Stephen
Grossberg (separate papers, not collaborations) a few years
back. They have a really neat concept: they get a paper,
then invite a number of noted scientists in the field to
praise it or trash it. They print these commentaries, and
give the author(s) a chance to make a rebuttal or
concurrence. Sometimes, as I'm sure you can imagine, things
get pretty lively. I'm reasonably sure they are still at
it--I think I saw them make a call for reviewers a few
months ago. Their reviewers are called something like
Behavioral and Brain Associates, and I believe they have to
be nominated by current associates, and should be fairly
well established in the field. That's probably more than I
really know about it but maybe if you post it someone who
knows more about it will correct any errors I have made.
The main thing is that I liked the articles I read. (Note:
remarks by Don Wunsch )
Title: International Journal of Applied Intelligence
Publish: Kluwer Academic Publishers
Remark: first issue in 1990(?)
Title: Bulletin of Mathematica Biology
Title: Intelligence
Title: Journal of Mathematical Biology
Title: Journal of Complex System
Title: AI Expert
Publish: Miller Freeman Publishing Co., for subscription call ++415-267-7672.
Remark: Regularly includes ANN related articles, product
announcements, and application reports. Listings of ANN
programs are available on AI Expert affiliated BBS's
Title: International Journal of Modern Physics C
Publish: USA: World Scientific Publishing Co., 1060 Main Street, River Edge,
NJ 07666. Tel: (201) 487 9655; Europe: World Scientific Publishing
Co. Ltd., 57 Shelton Street, London WC2H 9HE, England.
Tel: (0171) 836 0888; Asia: World Scientific Publishing Co. Pte. Ltd.,
1022 Hougang Avenue 1 #05-3520, Singapore 1953, Rep. of Singapore
Tel: 382 5663.
Freq: bi-monthly
Eds: H. Herrmann, R. Brower, G.C. Fox and S Nose
Title: Machine Learning
Publish: Kluwer Academic Publishers
Address: Kluwer Academic Publishers
P.O. Box 358
Accord Station
Hingham, MA 02018-0358 USA
Freq.: Monthly (8 issues per year; increasing to 12 in 1993)
Cost/Yr: Individual $140 (1992); Member of AAAI or CSCSI $88
Remark: Description: Machine Learning is an international forum for
research on computational approaches to learning. The journal
publishes articles reporting substantive research results on a
wide range of learning methods applied to a variety of task
domains. The ideal paper will make a theoretical contribution
supported by a computer implementation.
The journal has published many key papers in learning theory,
reinforcement learning, and decision tree methods. Recently
it has published a special issue on connectionist approaches
to symbolic reasoning. The journal regularly publishes
issues devoted to genetic algorithms as well.
Title: INTELLIGENCE - The Future of Computing
Published by: Intelligence
Address: INTELLIGENCE, P.O. Box 20008, New York, NY 10025-1510, USA,
212-222-1123 voice & fax; email: ier@aol.com, CIS: 72400,1013
Freq. Monthly plus four special reports each year (1st issue: May, 1984)
ISSN #: 1042-4296
Editor: Edward Rosenfeld
Cost/Yr: $395 (USA), US$450 (elsewhere)
Remark: Has absorbed several other newsletters, like Synapse/Connection
and Critical Technology Trends (formerly AI Trends).
Covers NN, genetic algorithms, fuzzy systems, wavelets, chaos
and other advanced computing approaches, as well as molecular
computing and nanotechnology.
Title: Journal of Physics A: Mathematical and General
Publish: Inst. of Physics, Bristol
Freq: 24 issues per year.
Remark: Statistical mechanics aspects of neural networks
(mostly Hopfield models).
Title: Physical Review A: Atomic, Molecular and Optical Physics
Publish: The American Physical Society (Am. Inst. of Physics)
Freq: Monthly
Remark: Statistical mechanics of neural networks.
Title: Information Sciences
Publish: North Holland (Elsevier Science)
Freq.: Monthly
ISSN: 0020-0255
Editor: Paul P. Wang; Department of Electrical Engineering; Duke University;
Durham, NC 27706, USA
C. Journals loosely related to NNs:
+++++++++++++++++++++++++++++++++++
Title: JOURNAL OF COMPLEXITY
Remark: (Must rank alongside Wolfram's Complex Systems)
Title: IEEE ASSP Magazine
Remark: (April 1987 had the Lippmann intro. which everyone likes to cite)
Title: ARTIFICIAL INTELLIGENCE
Remark: (Vol 40, September 1989 had the survey paper by Hinton)
Title: COGNITIVE SCIENCE
Remark: (the Boltzmann machine paper by Ackley et al appeared here
in Vol 9, 1983)
Title: COGNITION
Remark: (Vol 28, March 1988 contained the Fodor and Pylyshyn
critique of connectionism)
Title: COGNITIVE PSYCHOLOGY
Remark: (no comment!)
Title: JOURNAL OF MATHEMATICAL PSYCHOLOGY
Remark: (several good book reviews)
------------------------------------------------------------------------
14. A: The most important conferences concerned
===============================================
with Neural Networks?
=====================
[to be added: has taken place how often yet; most emphasized topics;
where to get proceedings/calls-for-papers etc. ]
A. Dedicated Neural Network Conferences:
++++++++++++++++++++++++++++++++++++++++
1. Neural Information Processing Systems (NIPS) Annually
since 1988 in Denver, Colorado; late November or early
December. Interdisciplinary conference with computer
science, physics, engineering, biology, medicine, cognitive
science topics. Covers all aspects of NNs. Proceedings
appear several months after the conference as a book from
Morgan Kaufman, San Mateo, CA.
2. International Joint Conference on Neural Networks
(IJCNN) formerly co-sponsored by INNS and IEEE, no
longer held.
3. Annual Conference on Neural Networks (ACNN)
4. International Conference on Artificial Neural Networks
(ICANN) Annually in Europe. First was 1991. Major
conference of European Neur. Netw. Soc. (ENNS)
5. WCNN. Sponsored by INNS.
6. European Symposium on Artificial Neural Networks
(ESANN). Anually since 1993 in Brussels, Belgium; late
April; conference on the fundamental aspects of artificial
neural networks: theory, mathematics, biology, relations
between neural networks and other disciplines, statistics,
learning, algorithms, models and architectures,
self-organization, signal processing, approximation of
functions, evolutive learning, etc. Contact: Michel
Verleysen, D facto conference services, 45 rue Masui,
B-1210 Brussels, Belgium, phone: +32 2 245 43 63, fax: +
32 2 245 46 94, e-mail: esann@dice.ucl.ac.be
7. Artificial Neural Networks in Engineering (ANNIE)
Anually since 1991 in St. Louis, Missouri; held in
November. (Topics: NN architectures, pattern recognition,
neuro-control, neuro-engineering systems. Contact:
ANNIE; Engineering Management Department; 223
Engineering Management Building; University of
Missouri-Rolla; Rolla, MO 65401; FAX: (314) 341-6567)
8. many many more....
B. Other Conferences
++++++++++++++++++++
1. International Joint Conference on Artificial Intelligence
(IJCAI)
2. Intern. Conf. on Acustics, Speech and Signal Processing
(ICASSP)
3. Intern. Conf. on Pattern Recognition. Held every other
year. Has a connectionist subconference. Information:
General Chair Walter G. Kropatsch
<krw@prip.tuwien.ac.at>
4. Annual Conference of the Cognitive Science Society
5. [Vision Conferences?]
C. Pointers to Conferences
++++++++++++++++++++++++++
1. The journal "Neural Networks" has a list of conferences,
workshops and meetings in each issue. This is quite
interdisciplinary.
2. There is a regular posting on comp.ai.neural-nets from
Paultje Bakker: "Upcoming Neural Network Conferences",
which lists names, dates, locations, contacts, and deadlines.
It is also available for anonymous ftp from ftp.cs.uq.oz.au
as /pub/pdp/conferences
------------------------------------------------------------------------
15. A: Neural Network Associations?
===================================
1. International Neural Network Society (INNS).
+++++++++++++++++++++++++++++++++++++++++++++++
INNS membership includes subscription to "Neural
Networks", the official journal of the society. Membership
is $55 for non-students and $45 for students per year.
Address: INNS Membership, P.O. Box 491166, Ft.
Washington, MD 20749.
2. International Student Society for Neural Networks
++++++++++++++++++++++++++++++++++++++++++++++++++++
(ISSNNets).
+++++++++++
Membership is $5 per year. Address: ISSNNet, Inc., P.O.
Box 15661, Boston, MA 02215 USA
3. Women In Neural Network Research and technology
++++++++++++++++++++++++++++++++++++++++++++++++++
(WINNERS).
++++++++++
Address: WINNERS, c/o Judith Dayhoff, 11141 Georgia
Ave., Suite 206, Wheaton, MD 20902. Phone:
301-933-9000.
4. European Neural Network Society (ENNS)
+++++++++++++++++++++++++++++++++++++++++
ENNS membership includes subscription to "Neural
Networks", the official journal of the society. Membership
is currently (1994) 50 UK pounds (35 UK pounds for
students) per year. Address: ENNS Membership, Centre for
Neural Networks, King's College London, Strand, London
WC2R 2LS, United Kingdom.
5. Japanese Neural Network Society (JNNS)
+++++++++++++++++++++++++++++++++++++++++
Address: Japanese Neural Network Society; Department of
Engineering, Tamagawa University; 6-1-1, Tamagawa
Gakuen, Machida City, Tokyo; 194 JAPAN; Phone: +81
427 28 3457, Fax: +81 427 28 3597
6. Association des Connexionnistes en THese (ACTH)
++++++++++++++++++++++++++++++++++++++++++++++++++
(the French Student Association for Neural Networks);
Membership is 100 FF per year; Activities : newsletter,
conference (every year), list of members, electronic forum;
Journal 'Valgo' (ISSN 1243-4825); Contact : acth@loria.fr
7. Neurosciences et Sciences de l'Ingenieur (NSI)
+++++++++++++++++++++++++++++++++++++++++++++++++
Biology & Computer Science Activity : conference (every
year) Address : NSI - TIRF / INPG 46 avenue Felix
Viallet 38031 Grenoble Cedex FRANCE
------------------------------------------------------------------------
16. A: Other sources of information about NNs?
==============================================
1. Neuron Digest
++++++++++++++++
Internet Mailing List. From the welcome blurb:
"Neuron-Digest is a list (in digest form) dealing with all
aspects of neural networks (and any type of network or
neuromorphic system)" To subscribe, send email to
neuron-request@cattell.psych.upenn.edu
comp.ai.neural-net readers also find the messages in that
newsgroup in the form of digests.
2. Usenet groups comp.ai.neural-nets (Oha!) and
+++++++++++++++++++++++++++++++++++++++++++++++
comp.theory.self-org-sys.
+++++++++++++++++++++++++
There is a periodic posting on comp.ai.neural-nets sent by
srctran@world.std.com (Gregory Aharonian) about Neural
Network patents.
3. Central Neural System Electronic Bulletin Board
++++++++++++++++++++++++++++++++++++++++++++++++++
Modem: 409-737-5222; Sysop: Wesley R. Elsberry; 4160
Pirates' Beach, Galveston, TX 77554;
welsberr@orca.tamu.edu. Many MS-DOS PD and
shareware simulations, source code, benchmarks,
demonstration packages, information files; some Unix,
Macintosh, Amiga related files. Also available are files on
AI, AI Expert listings 1986-1991, fuzzy logic, genetic
algorithms, artificial life, evolutionary biology, and many
Project Gutenberg and Wiretap etexts. No user fees have
ever been charged. Home of the NEURAL_NET Echo,
available thrugh FidoNet, RBBS-Net, and other EchoMail
compatible bulletin board systems.
4. Neural ftp archive site ftp.funet.fi
+++++++++++++++++++++++++++++++++++++++
Is administrating a large collection of neural network
papers and software at the Finnish University Network file
archive site ftp.funet.fi in directory /pub/sci/neural Contains
all the public domain software and papers that they have
been able to find. All of these files have been transferred
from FTP sites in U.S. and are mirrored about every 3
months at fastest. Contact: neural-adm@ftp.funet.fi
5. USENET newsgroup comp.org.issnnet
++++++++++++++++++++++++++++++++++++
Forum for discussion of academic/student-related issues in
NNs, as well as information on ISSNNet (see answer 12)
and its activities.
6. AI CD-ROM
++++++++++++
Network Cybernetics Corporation produces the "AI
CD-ROM". It is an ISO-9660 format CD-ROM and
contains a large assortment of software related to artificial
intelligence, artificial life, virtual reality, and other topics.
Programs for OS/2, MS-DOS, Macintosh, UNIX, and
other operating systems are included. Research papers,
tutorials, and other text files are included in ASCII, RTF,
and other universal formats. The files have been collected
from AI bulletin boards, Internet archive sites, University
computer deptartments, and other government and civilian
AI research organizations. Network Cybernetics
Corporation intends to release annual revisions to the AI
CD-ROM to keep it up to date with current developments
in the field. The AI CD-ROM includes collections of files
that address many specific AI/AL topics including Neural
Networks (Source code and executables for many different
platforms including Unix, DOS, and Macintosh. ANN
development tools, example networks, sample data,
tutorials. A complete collection of Neural Digest is included
as well.) The AI CD-ROM may be ordered directly by
check, money order, bank draft, or credit card from:
Network Cybernetics Corporation; 4201 Wingren Road
Suite 202; Irving, TX 75062-2763; Tel 214/650-2002; Fax
214/650-1929; The cost is $129 per disc + shipping ($5/disc
domestic or $10/disc foreign) (See the comp.ai FAQ for
further details)
7. NN events server
+++++++++++++++++++
There is a WWW page and an FTP Server for
Announcements of Conferences, Workshops and Other
Events on Neural Networks at IDIAP in Switzerland.
WWW-Server:
http://www.idiap.ch/html/idiap-networks.html,
FTP-Server: ftp://ftp.idiap.ch/html/NN-events/,
8. World Wide Web
+++++++++++++++++
In World-Wide-Web (WWW, for example via the xmosaic
program) you can read neural network information for
instance by opening one of the following uniform resource
locators (URLs): http://www.neuronet.ph.kcl.ac.uk
(NEuroNet, King's College, London),
http://www.eeb.ele.tue.nl (Eindhoven, Netherlands),
http://www.emsl.pnl.gov:2080/docs/cie/neural/ (Richland,
Washington),
http://www.cosy.sbg.ac.at/~rschwaig/rschwaig/projects.html
(Salzburg, Austria),
http://http2.sils.umich.edu/Public/nirg/nirg1.html
(Michigan). http://rtm.science.unitn.it/ Reactive Memory
Search (Tabu Search) page (Trento, Italy). Many others
are available too; WWW is changing all the time.
9. Neurosciences Internet Resource Guide
++++++++++++++++++++++++++++++++++++++++
This document aims to be a guide to existing, free,
Internet-accessible resources helpful to neuroscientists of
all stripes. An ASCII text version (86K) is available in the
Clearinghouse of Subject-Oriented Internet Resource
Guides as follows:
anonymous FTP, Gopher, WWW Hypertext
10. INTCON mailing list
+++++++++++++++++++++++
INTCON (Intelligent Control) is a moderated mailing list
set up to provide a forum for communication and exchange
of ideas among researchers in neuro-control, fuzzy logic
control, reinforcement learning and other related subjects
grouped under the topic of intelligent control. Send your
subscribe requests to
intcon-request@phoenix.ee.unsw.edu.au
------------------------------------------------------------------------
17. A: Freely available software packages for NN
================================================
simulation?
===========
1. Rochester Connectionist Simulator
++++++++++++++++++++++++++++++++++++
A quite versatile simulator program for arbitrary types of
neural nets. Comes with a backprop package and a
X11/Sunview interface. Available via anonymous FTP
from cs.rochester.edu in directory pub/packages/simulator
as the files README (8 KB), rcs_v4.2.tar.Z (2.9 MB),
2. UCLA-SFINX
+++++++++++++
ftp retina.cs.ucla.edu [131.179.16.6]; Login name: sfinxftp;
Password: joshua; directory: pub; files : README;
sfinx_v2.0.tar.Z; Email info request :
sfinx@retina.cs.ucla.edu
3. NeurDS
+++++++++
simulator for DEC systems supporting VT100 terminal.
available for anonymous ftp from gatekeeper.dec.com
[16.1.0.2] in directory: pub/DEC as the file
NeurDS031.tar.Z (111 Kb)
4. PlaNet5.7 (formerly known as SunNet)
+++++++++++++++++++++++++++++++++++++++
A popular connectionist simulator with versions to run
under X Windows, and non-graphics terminals created by
Yoshiro Miyata (Chukyo Univ., Japan). 60-page User's
Guide in Postscript. Send any questions to
miyata@sccs.chukyo-u.ac.jp Available for anonymous ftp
from ftp.ira.uka.de as /pub/neuron/PlaNet5.7.tar.Z (800 kb)
or from boulder.colorado.edu [128.138.240.1] as
/pub/generic-sources/PlaNet5.7.tar.Z
5. GENESIS
++++++++++
GENESIS 1.4.2 (GEneral NEural SImulation System) is a
general purpose simulation platform which was developed
to support the simulation of neural systems ranging from
complex models of single neurons to simulations of large
networks made up of more abstract neuronal components.
Most current GENESIS applications involve realistic
simulations of biological neural systems. Although the
software can also model more abstract networks, other
simulators are more suitable for backpropagation and
similar connectionist modeling. Available for ftp with the
following procedure: Use 'telnet' to genesis.bbb.caltech.edu
and login as the user "genesis" (no password). If you
answer all the questions, an 'ftp' account will
automatically be created for you. You can then 'ftp' back to
the machine and download the software (about 3 MB).
Contact: genesis@cns.caltech.edu. Further information via
WWW at http://www.bbb.caltech.edu/GENESIS/.
6. Mactivation
++++++++++++++
A neural network simulator for the Apple Macintosh.
Available for ftp from ftp.cs.colorado.edu [128.138.243.151]
as /pub/cs/misc/Mactivation-3.3.sea.hqx
7. Cascade Correlation Simulator
++++++++++++++++++++++++++++++++
A simulator for Scott Fahlman's Cascade Correlation
algorithm. Available for ftp from ftp.cs.cmu.edu
[128.2.206.173] in directory /afs/cs/project/connect/code as
the file cascor-v1.0.4.shar (218 KB) There is also a version
of recurrent cascade correlation in the same directory in
file rcc1.c (108 KB).
8. Quickprop
++++++++++++
A variation of the back-propagation algorithm developed
by Scott Fahlman. A simulator is available in the same
directory as the cascade correlation simulator above in file
nevprop1.16.shar (137 KB) (see also the description of
NEVPROP below)
9. DartNet
++++++++++
DartNet is a Macintosh-based backpropagation simulator,
developed at Dartmouth by Jamshed Bharucha and Sean
Nolan as a pedagogical tool. It makes use of the Mac's
graphical interface, and provides a number of tools for
building, editing, training, testing and examining networks.
This program is available by anonymous ftp from
dartvax.dartmouth.edu [129.170.16.4] as
/pub/mac/dartnet.sit.hqx (124 KB).
10. SNNS
++++++++
"Stuttgart Neural Network Simulator" from the University
of Stuttgart, Germany. A luxurious simulator for many
types of nets; with X11 interface: Graphical 2D and 3D
topology editor/visualizer, training visualisation, multiple
pattern set handling etc. Currently supports
backpropagation (vanilla, online, with momentum term
and flat spot elimination, batch, time delay),
counterpropagation, quickprop, backpercolation 1,
generalized radial basis functions (RBF), RProp, ART1,
ART2, ARTMAP, Cascade Correlation, Recurrent
Cascade Correlation, Dynamic LVQ, Backpropagation
through time (for recurrent networks), batch
backpropagation through time (for recurrent networks),
Quickpropagation through time (for recurrent networks),
Hopfield networks, Jordan and Elman networks,
autoassociative memory, self-organizing maps, time-delay
networks (TDNN), and is user-extendable (user-defined
activation functions, output functions, site functions,
learning procedures). Works on SunOS, Solaris, IRIX,
Ultrix, AIX, HP/UX, and Linux. Available for ftp from
ftp.informatik.uni-stuttgart.de [129.69.211.2] in directory
/pub/SNNS as SNNSv3.2.tar.Z (2 MB, Source code) and
SNNSv3.2.Manual.ps.Z (1.4 MB, Documentation). There
are also various other files in this directory (e.g. the source
version of the manual, a Sun Sparc executable, older
versions of the software, some papers, and the software in
several smaller parts). It may be best to first have a look at
the file SNNSv3.2.Readme (10 kb). This file contains a
somewhat more elaborate short description of the
simulator.
11. Aspirin/MIGRAINES
+++++++++++++++++++++
Aspirin/MIGRAINES 6.0 consists of a code generator that
builds neural network simulations by reading a network
description (written in a language called "Aspirin") and
generates a C simulation. An interface (called
"MIGRAINES") is provided to export data from the neural
network to visualization tools. The system has been ported
to a large number of platforms. The goal of Aspirin is to
provide a common extendible front-end language and
parser for different network paradigms. The MIGRAINES
interface is a terminal based interface that allows you to
open Unix pipes to data in the neural network. Users can
display the data using either public or commercial
graphics/analysis tools. Example filters are included that
convert data exported through MIGRAINES to formats
readable by Gnuplot 3.0, Matlab, Mathematica, and xgobi.
The software is available from two FTP sites: from CMU's
simulator collection on pt.cs.cmu.edu [128.2.254.155] in
/afs/cs/project/connect/code/am6.tar.Z and from UCLA's
cognitive science machine ftp.cognet.ucla.edu [128.97.50.19]
in /pub/alexis/am6.tar.Z (2 MB).
12. Adaptive Logic Network kit
++++++++++++++++++++++++++++++
This package differs from the traditional nets in that it uses
logic functions rather than floating point; for many tasks,
ALN's can show many orders of magnitude gain in
training and performance speed. Anonymous ftp from
menaik.cs.ualberta.ca [129.128.4.241] in directory
/pub/atree. See the files README (7 KB), atree2.tar.Z
(145 kb, Unix source code and examples), atree2.ps.Z (76
kb, documentation), a27exe.exe (412 kb, MS-Windows 3.x
executable), atre27.exe (572 kb, MS-Windows 3.x source
code).
13. NeuralShell
+++++++++++++++
Formerly available from FTP site
quanta.eng.ohio-state.edu [128.146.35.1] as
/pub/NeuralShell/NeuralShell.tar". Currently (April 94)
not available and undergoing a major reconstruction. Not
to be confused with NeuroShell by Ward System Group
(see below under commercial software).
14. PDP
+++++++
The PDP simulator package is available via anonymous
FTP at nic.funet.fi [128.214.6.100] as
/pub/sci/neural/sims/pdp.tar.Z (202 kb). The simulator is
also available with the book "Explorations in Parallel
Distributed Processing: A Handbook of Models, Programs,
and Exercises" by McClelland and Rumelhart. MIT Press,
1988. Comment: "This book is often referred to as PDP vol
III which is a very misleading practice! The book comes
with software on an IBM disk but includes a makefile for
compiling on UNIX systems. The version of PDP available
at ftp.funet.fi seems identical to the one with the book
except for a bug in bp.c which occurs when you try to run a
script of PDP commands using the DO command. This can
be found and fixed easily."
15. Xerion
++++++++++
Xerion runs on SGI and Sun machines and uses X
Windows for graphics. The software contains modules that
implement Back Propagation, Recurrent Back Propagation,
Boltzmann Machine, Mean Field Theory, Free Energy
Manipulation, Hard and Soft Competitive Learning, and
Kohonen Networks. Sample networks built for each of the
modules are also included. Contact: xerion@ai.toronto.edu.
Xerion is available via anonymous ftp from
ftp.cs.toronto.edu [128.100.1.105] in directory /pub/xerion as
xerion-3.1.ps.Z (153 kB) and xerion-3.1.tar.Z (1.3 MB)
plus several concrete simulators built with xerion (about 40
kB each).
16. Neocognitron simulator
++++++++++++++++++++++++++
The simulator is written in C and comes with a list of
references which are necessary to read to understand the
specifics of the implementation. The unsupervised version
is coded without (!) C-cell inhibition. Available for
anonymous ftp from unix.hensa.ac.uk [129.12.21.7] in
/pub/neocognitron.tar.Z (130 kB).
17. Multi-Module Neural Computing Environment
+++++++++++++++++++++++++++++++++++++++++++++
(MUME)
++++++
MUME is a simulation environment for multi-modules
neural computing. It provides an object oriented facility for
the simulation and training of multiple nets with various
architectures and learning algorithms. MUME includes a
library of network architectures including feedforward,
simple recurrent, and continuously running recurrent
neural networks. Each architecture is supported by a
variety of learning algorithms. MUME can be used for
large scale neural network simulations as it provides
support for learning in multi-net environments. It also
provide pre- and post-processing facilities. The modules
are provided in a library. Several "front-ends" or clients
are also available. X-Window support by
editor/visualization tool Xmume. MUME can be used to
include non-neural computing modules (decision trees, ...)
in applications. MUME is available anonymous ftp on
mickey.sedal.su.oz.au [129.78.24.170] after signing and
sending a licence: /pub/license.ps (67 kb). Contact: Marwan
Jabri, SEDAL, Sydney University Electrical Engineering,
NSW 2006 Australia, marwan@sedal.su.oz.au
18. LVQ_PAK, SOM_PAK
++++++++++++++++++++
These are packages for Learning Vector Quantization and
Self-Organizing Maps, respectively. They have been built
by the LVQ/SOM Programming Team of the Helsinki
University of Technology, Laboratory of Computer and
Information Science, Rakentajanaukio 2 C, SF-02150
Espoo, FINLAND There are versions for Unix and
MS-DOS available from cochlea.hut.fi [130.233.168.48] as
/pub/lvq_pak/lvq_pak-2.1.tar.Z (340 kB, Unix sources),
/pub/lvq_pak/lvq_p2r1.exe (310 kB, MS-DOS self-extract
archive), /pub/som_pak/som_pak-1.2.tar.Z (251 kB, Unix
sources), /pub/som_pak/som_p1r2.exe (215 kB, MS-DOS
self-extract archive). (further programs to be used with
SOM_PAK and LVQ_PAK can be found in /pub/utils).
19. SESAME
++++++++++
("Software Environment for the Simulation of Adaptive
Modular Systems") SESAME is a prototypical software
implementation which facilitates
o Object-oriented building blocks approach.
o Contains a large set of C++ classes useful for neural
nets, neurocontrol and pattern recognition. No C++
classes can be used as stand alone, though!
o C++ classes include CartPole, nondynamic
two-robot arms, Lunar Lander, Backpropagation,
Feature Maps, Radial Basis Functions,
TimeWindows, Fuzzy Set Coding, Potential Fields,
Pandemonium, and diverse utility building blocks.
o A kernel which is the framework for the C++
classes and allows run-time manipulation,
construction, and integration of arbitrary complex
and hybrid experiments.
o Currently no graphic interface for construction, only
for visualization.
o Platform is SUN4, XWindows
Unfortunately no reasonable good introduction has been
written until now. We hope to have something soon. For
now we provide papers (eg. NIPS-92), a reference manual
(>220 pages), source code (ca. 35.000 lines of code), and a
SUN4-executable by ftp only. Sesame and its description is
available in various files for anonymous ftp on ftp
ftp.gmd.de in the directories /gmd/as/sesame and
/gmd/as/paper. Questions to sesame-request@gmd.de; there
is only very limited support available.
20. Nevada Backpropagation (NevProp)
++++++++++++++++++++++++++++++++++++
NevProp is a free, easy-to-use feedforward
backpropagation (multilayer perceptron) program. It uses
an interactive character-based interface, and is distributed
as C source code that should compile and run on most
platforms. (Precompiled executables are available for
Macintosh and DOS.) The original version was Quickprop
1.0 by Scott Fahlman, as translated from Common Lisp by
Terry Regier. We added early-stopped training based on a
held-out subset of data, c index (ROC curve area)
calculation, the ability to force gradient descent (per-epoch
or per-pattern), and additional options. FEATURES
(NevProp version 1.16): UNLIMITED (except by machine
memory) number of input PATTERNS; UNLIMITED
number of input, hidden, and output UNITS; Arbitrary
CONNECTIONS among the various layers' units;
Clock-time or user-specified RANDOM SEED for initial
random weights; Choice of regular GRADIENT
DESCENT or QUICKPROP; Choice of PER-EPOCH or
PER-PATTERN (stochastic) weight updating;
GENERALIZATION to a test dataset;
AUTOMATICALLY STOPPED TRAINING based on
generalization; RETENTION of best-generalizing weights
and predictions; Simple but useful GRAPHIC display to
show smoothness of generalization; SAVING of results to
a file while working interactively; SAVING of weights file
and reloading for continued training; PREDICTION-only
on datasets by applying an existing weights file; In addition
to RMS error, the concordance, or c index is displayed. The
c index (area under the ROC curve) shows the correctness
of the RELATIVE ordering of predictions AMONG the
cases; ie, it is a measure of discriminative power of the
model. AVAILABILITY: The most updated version of
NevProp will be made available by anonymous ftp from the
University of Nevada, Reno: On ftp.scs.unr.edu
[134.197.10.130] in the directory
"pub/goodman/nevpropdir", e.g. README.FIRST (45 kb)
or nevprop1.16.shar (138 kb). Version 2 (not yet released)
is intended to have some new features: more flexible file
formatting (including access to external data files; option to
prerandomize data order; randomized stochastic gradient
descent; option to rescale predictor (input) variables);
linear output units as an alternative to sigmoidal units for
use with continuous-valued dependent variables (output
targets); cross-entropy (maximum likelihood) criterion
function as an alternative to square error for use with
categorical dependent variables
(classification/symbolic/nominal targets); and interactive
interrupt to change settings on-the-fly. Limited support is
available from Phil Goodman (goodman@unr.edu),
University of Nevada Center for Biomedical Research.
21. Fuzzy ARTmap
++++++++++++++++
This is just a small example program. Available for
anonymous ftp from park.bu.edu [128.176.121.56]
/pub/fuzzy-artmap.tar.Z (44 kB).
22. PYGMALION
+++++++++++++
This is a prototype that stems from an ESPRIT project. It
implements back-propagation, self organising map, and
Hopfield nets. Avaliable for ftp from ftp.funet.fi
[128.214.248.6] as /pub/sci/neural/sims/pygmalion.tar.Z
(1534 kb). (Original site is imag.imag.fr:
archive/pygmalion/pygmalion.tar.Z).
23. Basis-of-AI-backprop
++++++++++++++++++++++++
Earlier versions have been posted in comp.sources.misc and
people around the world have used them and liked them.
This package is free for ordinary users but shareware for
businesses and government agencies ($200/copy, but then
for this you get the professional version as well). I do
support this package via email. Some of the highlights are:
o in C for UNIX and DOS and DOS binaries
o gradient descent, delta-bar-delta and quickprop
o extra fast 16-bit fixed point weight version as well
as a conventional floating point version
o recurrent networks
o numerous sample problems
Available for ftp from ftp.mcs.com in directory
/mcsnet.users/drt. Or see the WWW page
http://www.mcs.com/~drt/home.html. The expanded
professional version is $30/copy for ordinary individuals
including academics and $200/copy for businesses and
government agencies (improved user interface, more
activation functions, networks can be read into your own
programs, dynamic node creation, weight decay,
SuperSAB). More details can be found in the
documentation for the student version. Contact: Don
Tveter; 5228 N. Nashville Ave.; Chicago, Illinois 60656;
drt@mcs.com
24. Matrix Backpropagation
++++++++++++++++++++++++++
MBP (Matrix Back Propagation) is a very efficient
implementation of the back-propagation algorithm for
current-generation workstations. The algorithm includes a
per-epoch adaptive technique for gradient descent. All the
computations are done through matrix multiplications and
make use of highly optimized C code. The goal is to reach
almost peak-performances on RISCs with superscalar
capabilities and fast caches. On some machines (and with
large networks) a 30-40x speed-up can be measured with
respect to conventional implementations. The software is
available by anonymous ftp from risc6000.dibe.unige.it
[130.251.89.154] as /pub/MBPv1.1.tar.Z (Unix version),
/pub/MBPv11.zip.Z (MS-DOS version), /pub/mpbv11.ps
(Documentation). For more information, contact Davide
Anguita (anguita@dibe.unige.it).
25. WinNN
+++++++++
WinNN is a shareware Neural Networks (NN) package for
windows 3.1. WinNN incorporates a very user friendly
interface with a powerful computational engine. WinNN is
intended to be used as a tool for beginners and more
advanced neural networks users, it provides an alternative
to using more expensive and hard to use packages. WinNN
can implement feed forward multi-layered NN and uses a
modified fast back-propagation for training. Extensive on
line help. Has various neuron functions. Allows on the fly
testing of the network performance and generalization. All
training parameters can be easily modified while WinNN is
training. Results can be saved on disk or copied to the
clipboard. Supports plotting of the outputs and weight
distribution. Available for ftp from winftp.cica.indiana.edu
as /pub/pc/win3/programr/winnn093.zip (545 kB).
26. BIOSIM
++++++++++
BIOSIM is a biologically oriented neural network
simulator. Public domain, runs on Unix (less powerful
PC-version is available, too), easy to install, bilingual
(german and english), has a GUI (Graphical User
Interface), designed for research and teaching, provides
online help facilities, offers controlling interfaces, batch
version is available, a DEMO is provided.
REQUIREMENTS (Unix version): X11 Rel. 3 and above,
Motif Rel 1.0 and above, 12 MB of physical memory,
recommended are 24 MB and more, 20 MB disc space.
REQUIREMENTS (PC version): PC-compatible with MS
Windows 3.0 and above, 4 MB of physical memory,
recommended are 8 MB and more, 1 MB disc space. Four
neuron models are implemented in BIOSIM: a simple
model only switching ion channels on and off, the original
Hodgkin-Huxley model, the SWIM model (a modified HH
model) and the Golowasch-Buchholz model. Dendrites
consist of a chain of segments without bifurcation. A
neural network can be created by using the interactive
network editor which is part of BIOSIM. Parameters can
be changed via context sensitive menus and the results of
the simulation can be visualized in observation windows
for neurons and synapses. Stochastic processes such as
noise can be included. In addition, biologically orientied
learning and forgetting processes are modeled, e.g.
sensitization, habituation, conditioning, hebbian learning
and competitive learning. Three synaptic types are
predefined (an excitatatory synapse type, an inhibitory
synapse type and an electrical synapse). Additional synaptic
types can be created interactively as desired. Available for
ftp from ftp.uni-kl.de in directory /pub/bio/neurobio: Get
/pub/bio/neurobio/biosim.readme (2 kb) and
/pub/bio/neurobio/biosim.tar.Z (2.6 MB) for the Unix
version or /pub/bio/neurobio/biosimpc.readme (2 kb) and
/pub/bio/neurobio/biosimpc.zip (150 kb) for the PC version.
Contact: Stefan Bergdoll; Department of Software
Engineering (ZXA/US); BASF Inc.; D-67056
Ludwigshafen; Germany; bergdoll@zxa.basf-ag.de; phone
0621-60-21372; fax 0621-60-43735
27. The Brain
+++++++++++++
The Brain is an advanced neural network simulator for
PCs that is simple enough to be used by non-technical
people, yet sophisticated enough for serious research work.
It is based upon the backpropagation learning algorithm.
Three sample networks are included. The documentation
included provides you with an introduction and overview of
the concepts and applications of neural networks as well as
outlining the features and capabilities of The Brain. The
Brain requires 512K memory and MS-DOS or PC-DOS
version 3.20 or later (versions for other OS's and machines
are available). A 386 (with maths coprocessor) or higher is
recommended for serious use of The Brain. Shareware
payment required. Demo version is restricted to number of
units the network can handle due to memory contraints on
PC's. Registered version allows use of extra memory.
External documentation included: 39Kb, 20 Pages. Source
included: No (Source comes with registration). Available
via anonymous ftp from ftp.tu-clausthal.de as
/pub/msdos/science/brain12.zip (78 kb) and from
ftp.technion.ac.il as /pub/contrib/dos/brain12.zip (78 kb)
Contact: David Perkovic; DP Computing; PO Box 712;
Noarlunga Center SA 5168; Australia; Email:
dip@mod.dsto.gov.au (preferred) or dpc@mep.com or
perkovic@cleese.apana.org.au
28. FuNeGen 1.0
+++++++++++++++
FuNeGen is a MLP based software program to generate
fuzzy rule based classifiers. A limited version (maximum of
7 inputs and 3 membership functions for each input) for
PCs is available for anonymous ftp from
obelix.microelectronic.e-technik.th-darmstadt.de in
directory /pub/neurofuzzy. For further information see the
file read.me. Contact: Saman K. Halgamuge
29. NeuDL -- Neural-Network Description Language
++++++++++++++++++++++++++++++++++++++++++++++++
NeuDL is a description language for the design, training,
and operation of neural networks. It is currently limited to
the backpropagation neural-network model; however, it
offers a great deal of flexibility. For example, the user can
explicitly specify the connections between nodes and can
create or destroy connections dynamically as training
progresses. NeuDL is an interpreted language resembling C
or C++. It also has instructions dealing with
training/testing set manipulation as well as neural network
operation. A NeuDL program can be run in interpreted
mode or it can be automatically translated into C++ which
can be compiled and then executed. The NeuDL interpreter
is written in C++ and can be easly extended with new
instructions. NeuDL is available from the anonymous ftp
site at The University of Alabama: cs.ua.edu (130.160.44.1)
in the file /pub/neudl/NeuDLver021.tar. The tarred file
contains the interpreter source code (in C++) a user
manual, a paper about NeuDL, and about 25 sample
NeuDL programs. A document demonstrating NeuDL's
capabilities is also available from the ftp site:
/pub/neudl/NeuDL/demo.doc /pub/neudl/demo.doc. For
more information contact the author: Joey Rogers
(jrogers@buster.eng.ua.edu).
30. NeoC Explorer (Pattern Maker included)
++++++++++++++++++++++++++++++++++++++++++
The NeoC software is an implementation of Fukushima's
Neocognitron neural network. Its purpose is to test the
model and to facilitate interactivity for the experiments.
Some substantial features: GUI, explorer and tester
operation modes, recognition statistics, performance
analysis, elements displaying, easy net construction. PLUS,
a pattern maker utility for testing ANN: GUI, text file
output, transformations. Available for anonymous FTP
from OAK.Oakland.Edu (141.210.10.117) as
/SimTel/msdos/neurlnet/neocog10.zip (193 kB, DOS
version)
31. AINET
+++++++++
aiNet is a shareware Neural Networks (NN) application
for MS-Windows 3.1. It does not require learning, has no
limits in parameters (input & output neurons), no limits in
sample size. It is not sensitive toward noise in the data.
Database can be changed dynamically. It provides a way to
estimate the rate of error in your prediction. Missing values
are handled automatically. It has graphical
spreadsheet-like user interface and on-line help system. It
provides also several different charts types. aiNet manual
(90 pages) is divided into: "User's Guide", "Basics About
Modeling with the AINET", "Examples". Special
requirements: Windows 3.1, VGA or better. Can be
downloaded from
ftp://ftp.cica.indiana.edu/pub/pc/win3/programr/ainet100.zip
or from
ftp://oak.oakland.edu/SimTel/win3/math/ainet100.zip
For some of these simulators there are user mailing lists. Get the
packages and look into their documentation for further info.
If you are using a small computer (PC, Mac, etc.) you may want
to have a look at the Central Neural System Electronic Bulletin
Board (see answer 13). Modem: 409-737-5312; Sysop: Wesley R.
Elsberry; 4160 Pirates' Beach, Galveston, TX, USA;
welsberr@orca.tamu.edu. There are lots of small simulator
packages, the CNS ANNSIM file set. There is an ftp mirror site
for the CNS ANNSIM file set at me.uta.edu [129.107.2.20] in the
/pub/neural directory. Most ANN offerings are in
/pub/neural/annsim.
------------------------------------------------------------------------
18. A: Commercial software packages for NN
==========================================
simulation?
===========
1. nn/xnn
+++++++++
Name: nn/xnn
Company: Neureka ANS
Address: Klaus Hansens vei 31B
5037 Solheimsviken
NORWAY
Phone: +47-55544163 / +47-55201548
Email: arnemo@eik.ii.uib.no
Basic capabilities:
Neural network development tool. nn is a language for specification of
neural network simulators. Produces C-code and executables for the
specified models, therefore ideal for application development. xnn is
a graphical front-end to nn and the simulation code produced by nn.
Gives graphical representations in a number of formats of any
variables during simulation run-time. Comes with a number of
pre-implemented models, including: Backprop (several variants), Self
Organizing Maps, LVQ1, LVQ2, Radial Basis Function Networks,
Generalized Regression Neural Networks, Jordan nets, Elman nets,
Hopfield, etc.
Operating system: nn: UNIX or MS-DOS, xnn: UNIX/X-windows
System requirements: 10 Mb HD, 2 Mb RAM
Approx. price: USD 2000,-
2. BrainMaker
+++++++++++++
Name: BrainMaker, BrainMaker Pro
Company: California Scientific Software
Address: 10024 Newtown rd, Nevada City, CA, 95959 USA
Phone,Fax: 916 478 9040, 916 478 9041
Email: calsci!mittmann@gvgpsa.gvg.tek.com (flakey connection)
Basic capabilities: train backprop neural nets
Operating system: DOS, Windows, Mac
System requirements:
Uses XMS or EMS for large models(PCs only): Pro version
Approx. price: $195, $795
BrainMaker Pro 3.0 (DOS/Windows) $795
Gennetic Training add-on $250
ainMaker 3.0 (DOS/Windows/Mac) $195
Network Toolkit add-on $150
BrainMaker 2.5 Student version (quantity sales only, about $38 each)
BrainMaker Pro C30 Accelerator Board
w/ 5Mb memory $9750
w/32Mb memory $13,000
Intel iNNTS NN Development System $11,800
Intel EMB Multi-Chip Board $9750
Intel 80170 chip set $940
Introduction To Neural Networks book $30
California Scientific Software can be reached at:
Phone: 916 478 9040 Fax: 916 478 9041 Tech Support: 916 478 9035
Mail: 10024 newtown rd, Nevada City, CA, 95959, USA
30 day money back guarantee, and unlimited free technical support.
BrainMaker package includes:
The book Introduction to Neural Networks
BrainMaker Users Guide and reference manual
300 pages , fully indexed, with tutorials, and sample networks
Netmaker
Netmaker makes building and training Neural Networks easy, by
importing and automatically creating BrainMaker's Neural Network
files. Netmaker imports Lotus, Excel, dBase, and ASCII files.
BrainMaker
Full menu and dialog box interface, runs Backprop at 750,000 cps
on a 33Mhz 486.
---Features ("P" means is avaliable in professional version only):
Pull-down Menus, Dialog Boxes, Programmable Output Files,
Editing in BrainMaker, Network Progress Display (P),
Fact Annotation, supports many printers, NetPlotter,
Graphics Built In (P), Dynamic Data Exchange (P),
Binary Data Mode, Batch Use Mode (P), EMS and XMS Memory (P),
Save Network Periodically, Fastest Algorithms,
512 Neurons per Layer (P: 32,000), up to 8 layers,
Specify Parameters by Layer (P), Recurrence Networks (P),
Prune Connections and Neurons (P), Add Hidden Neurons In Training,
Custom Neuron Functions, Testing While Training,
Stop training when...-function (P), Heavy Weights (P),
Hypersonic Training, Sensitivity Analysis (P), Neuron Sensitivity (P),
Global Network Analysis (P), Contour Analysis (P),
Data Correlator (P), Error Statistics Report,
Print or Edit Weight Matrices, Competitor (P), Run Time System (P),
Chip Support for Intel, American Neurologics, Micro Devices,
Genetic Training Option (P), NetMaker, NetChecker,
Shuffle, Data Import from Lotus, dBASE, Excel, ASCII, binary,
Finacial Data (P), Data Manipulation, Cyclic Analysis (P),
User's Guide quick start booklet,
Introduction to Neural Networks 324 pp book
3. SAS Software/ Neural Net add-on
++++++++++++++++++++++++++++++++++
Name: SAS Software
Company: SAS Institute, Inc.
Address: SAS Campus Drive, Cary, NC 27513, USA
Phone,Fax: (919) 677-8000
Email: saswss@unx.sas.com (Neural net inquiries only)
Basic capabilities:
Feedforward nets with numerous training methods
and loss functions, plus statistical analogs of
counterpropagation and various unsupervised
architectures
Operating system: Lots
System requirements: Lots
Uses XMS or EMS for large models(PCs only): Runs under Windows, OS/2
Approx. price: Free neural net software, but you have to license
SAS/Base software and preferably the SAS/OR, SAS/ETS,
and/or SAS/STAT products.
Comments: Oriented toward data analysis and statistical applications
4. NeuralWorks
++++++++++++++
Name: NeuralWorks Professional II Plus (from NeuralWare)
Company: NeuralWare Inc.
Adress: Pittsburgh, PA 15276-9910
Phone: (412) 787-8222
FAX: (412) 787-8220
Distributor for Europe:
Scientific Computers GmbH.
Franzstr. 107, 52064 Aachen
Germany
Tel. (49) +241-26041
Fax. (49) +241-44983
Email. info@scientific.de
Basic capabilities:
supports over 30 different nets: backprop, art-1,kohonen,
modular neural network, General regression, Fuzzy art-map,
probabilistic nets, self-organizing map, lvq, boltmann,
bsb, spr, etc...
Extendable with optional package.
ExplainNet, Flashcode (compiles net in .c code for runtime),
user-defined io in c possible. ExplainNet (to eliminate
extra inputs), pruning, savebest,graph.instruments like
correlation, hinton diagrams, rms error graphs etc..
Operating system : PC,Sun,IBM RS6000,Apple Macintosh,SGI,Dec,HP.
System requirements: varies. PC:2MB extended memory+6MB Harddisk space.
Uses windows compatible memory driver (extended).
Uses extended memory.
Approx. price : call (depends on platform)
Comments : award winning documentation, one of the market
leaders in NN software.
5. MATLAB Neural Network Toolbox (for use with Matlab
+++++++++++++++++++++++++++++++++++++++++++++++++++++
4.x)
++++
Contact: The MathWorks, Inc. Phone: 508-653-1415
24 Prime Park Way FAX: 508-653-2997
Natick, MA 01760 email: info@mathworks.com
The Neural Network Toolbox is a powerful collection of
MATLAB functions for the design, training, and
simulation of neural networks. It supports a wide range of
network architectures with an unlimited number of
processing elements and interconnections (up to operating
system constraints). Supported architectures and training
methods include: supervised training of feedforward
networks using the perceptron learning rule, Widrow-Hoff
rule, several variations on backpropagation (including the
fast Levenberg-Marquardt algorithm), and radial basis
networks; supervised training of recurrent Elman
networks; unsupervised training of associative networks
including competitive and feature map layers; Kohonen
networks, self-organizing maps, and learning vector
quantization. The Neural Network Toolbox contains a
textbook-quality Users' Guide, uses tutorials, reference
materials and sample applications with code examples to
explain the design and use of each network architecture
and paradigm. The Toolbox is delivered as MATLAB
M-files, enabling users to see the algorithms and
implementations, as well as to make changes or create new
functions to address a specific application.
(Comment by Richard Andrew Miles Outerbridge,
RAMO@UVPHYS.PHYS.UVIC.CA:) Matlab is spreading
like hotcakes (and the educational discounts are very
impressive). The newest release of Matlab (4.0) ansrwers
the question "if you could only program in one language
what would it be?". The neural network toolkit is worth
getting for the manual alone. Matlab is available with lots
of other toolkits (signal processing, optimization, etc.) but I
don't use them much - the main package is more than
enough. The nice thing about the Matlab approach is that
you can easily interface the neural network stuff with
anything else you are doing.
6. Propagator
+++++++++++++
Contact: ARD Corporation,
9151 Rumsey Road, Columbia, MD 21045, USA
propagator@ard.com
Easy to use neural network training package. A GUI implementation of
backpropagation networks with five layers (32,000 nodes per layer).
Features dynamic performance graphs, training with a validation set,
and C/C++ source code generation.
For Sun (Solaris 1.x & 2.x, $499),
PC (Windows 3.x, $199)
Mac (System 7.x, $199)
Floating point coprocessor required, Educational Discount,
Money Back Guarantee, Muliti User Discount
Windows Demo on:
nic.funet.fi /pub/msdos/windows/demo
oak.oakland.edu /pub/msdos/neural_nets
gatordem.zip pkzip 2.04g archive file
gatordem.txt readme text file
7. NeuroForecaster
++++++++++++++++++
Name: NeuroForecaster(TM)/Genetica 3.1
Contact: Accel Infotech (S) Pte Ltd; 648 Geylang Road;
Republic of Singapore 1438; Phone: +65-7446863; Fax: +65-7492467
accel@solomon.technet.sg
For IBM PC 386/486 with mouse, or compatibles MS Windows* 3.1,
MS DOS 5.0 or above 4 MB RAM, 5 MB available harddisk space min;
3.5 inch floppy drive, VGA monitor or above, Math coprocessor recommended.
Neuroforecaster 3.1 for Windows is priced at US$1199 per single user
license. Please email us (accel@solomon.technet.sg) for order form.
More information about NeuroForecaster(TM)/Genetical may be found in
ftp://ftp.technet.sg/Technet/user/accel/nfga40.exe
NeuroForecaster is a user-friendly neural network program specifically
designed for building sophisticated and powerful forecasting and
decision-support systems (Time-Series Forecasting, Cross-Sectional
Classification, Indicator Analysis)
Features:
* GENETICA Net Builder Option for automatic network optimization
* 12 Neuro-Fuzzy Network Models
* Multitasking & Background Training Mode
* Unlimited Network Capacity
* Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market
Cycles & Check for Predictability
* Correlation Analysis to Compute Correlation Factors to Analyze the
Significance of Indicators
* Weight Histogram to Monitor the Progress of Learning
* Accumulated Error Analysis to Analyze the Strength of Input Indicators
Its user-friendly interface allows the users to build applications quickly,
easily and interactively, analyze the data visually and see the results
immediately.
The following example applications are included in the package:
* Credit Rating - for generating the credit rating of bank loan
applications.
* Stock market 6 monthly returns forecast
* Stock selection based on company ratios
* US$ to Deutschmark exchange rate forecast
* US$ to Yen exchange rate forecast
* US$ to SGD exchange rate forecast
* Property price valuation
* XOR - a classical problem to show the results are better than others
* Chaos - Prediction of Mackey-Glass chaotic time series
* SineWave - For demonstrating the power of Rescaled Range Analysis and
significance of window size
Techniques Implemented:
* GENETICA Net Builder Option - network creation & optimization based on
Darwinian evolution theory
* Backprop Neural Networks - the most widely-used training algorithm
* Fastprop Neural Networks - speeds up training of large problems
* Radial Basis Function Networks - best for pattern classification problems
* Neuro-Fuzzy Network
* Rescaled Range Analysis - computes Hurst exponents to unveil hidden
cycles & check for predictability
* Correlation Analysis - to identify significant input indicators
8. Products of NESTOR, Inc.
+++++++++++++++++++++++++++
530 Fifth Avenue; New York, NY 10036; USA; Tel.:
001-212-398-7955
Founders: Dr. Leon Cooper (having a Nobel Price) and Dr.
Charles Elbaum (Brown University). Neural Network
Models: Adaptive shape and pattern recognition (Restricted
Coulomb Energy - RCE) developed by NESTOR is one of
the most powerfull Neural Network Model used in a later
products. The basis for NESTOR products is the Nestor
Learning System - NLS. Later are developed: Character
Learning System - CLS and Image Learning System -
ILS. Nestor Development System - NDS is a development
tool in Standard C - one of the most powerfull PC-Tools
for simulation and development of Neural Networks. NLS
is a multi-layer, feed forward system with low connectivity
within each layer and no relaxation procedure used for
determining an output response. This unique architecture
allows the NLS to operate in real time without the need for
special computers or custom hardware. NLS is composed of
multiple neural networks, each specializing in a subset of
information about the input patterns. The NLS integrates
the responses of its several parallel networks to produce a
system response that is far superior to that of other neural
networks. Minimized connectivity within each layer results
in rapid training and efficient memory utilization- ideal for
current VLSI technology. Intel has made such a chip -
NE1000.
9. NeuroShell2/NeuroWindows
+++++++++++++++++++++++++++
NeuroShell 2 combines powerful neural network
architectures, a Windows icon driven user interface, and
sophisticated utilities for MS-Windows machines. Internal
format is spreadsheet, and users can specify that
NeuroShell 2 use their own spreadsheet when editing.
Includes both Beginner's and Advanced systems, a
Runtime capability, and a choice of 15 Backpropagation,
Kohonen, PNN and GRNN architectures. Includes Rules,
Symbol Translate, Graphics, File Import/Export modules
(including MetaStock from Equis International) and
NET-PERFECT to prevent overtraining. Options
available: Market Technical Indicator Option ($295),
Market Technical Indicator Option with Optimizer ($590),
and Race Handicapping Option ($149). NeuroShell price:
$495.
NeuroWindows is a programmer's tool in a Dynamic Link
Library (DLL) that can create as many as 128 interactive
nets in an application, each with 32 slabs in a single
network, and 32K neurons in a slab. Includes
Backpropagation, Kohonen, PNN, and GRNN paradigms.
NeuroWindows can mix supervised and unsupervised nets.
The DLL may be called from Visual Basic, Visual C,
Access Basic, C, Pascal, and VBA/Excel 5. NeuroWindows
price: $369.
Contact: Ward Systems Group, Inc.; Executive Park West;
5 Hillcrest Drive; Frederick, MD 21702; USA; Phone: 301
662-7950; FAX: 301 662-5666. Contact us for a free demo
diskette and Consumer's Guide to Neural Networks.
10. NuTank
++++++++++
NuTank stands for NeuralTank. It is educational and
entertainment software. In this program one is given the
shell of a 2 dimentional robotic tank. The tank has various
I/O devices like wheels, whiskers, optical sensors, smell, fuel
level, sound and such. These I/O sensors are connected to
Neurons. The player/designer uses more Neurons to
interconnect the I/O devices. One can have any level of
complexity desired (memory limited) and do subsumptive
designs. More complex design take slightly more fuel, so life
is not free. All movement costs fuel too. One can also tag
neuron connections as "adaptable" that adapt their weights
in acordance with the target neuron. This allows neurons
to learn. The Neuron editor can handle 3 dimention arrays
of neurons as single entities with very flexible interconect
patterns.
One can then design a scenario with walls, rocks, lights, fat
(fuel) sources (that can be smelled) and many other such
things. Robot tanks are then introduced into the Scenario
and allowed interact or battle it out. The last one alive
wins, or maybe one just watches the motion of the robots
for fun. While the scenario is running it can be stopped,
edited, zoom'd, and can track on any robot.
The entire program is mouse and graphicly based. It uses
DOS and VGA and is written in TurboC++. There will
also be the ability to download designs to another computer
and source code will be available for the core neural
simulator. This will allow one to design neural systems and
download them to real robots. The design tools can handle
three dimentional networks so will work with video camera
inputs and such. Eventualy I expect to do a port to UNIX
and multi thread the sign. I also expect to do a Mac port
and maybe NT or OS/2
Copies of NuTank cost $50 each. Contact: Richard Keene;
Keene Educational Software;
Dick.Keene@Central.Sun.COM
NuTank shareware with the Save options disabled is
available via anonymous ftp from the Internet, see the file
/pub/incoming/nutank.readme on the host
cher.media.mit.edu.
11. Neuralyst
+++++++++++++
Name: Neuralyst Version 1.4; Company: Cheshire
Engineering Corporation; Address: 650 Sierra Madre Villa,
Suite 201, Pasedena CA 91107; Phone: 818-351-0209;
Fax: 818-351-8645;
Basic capabilities: training of backpropogation neural nets.
Operating system: Windows or Macintosh running
Microsoft Excel Spreadsheet. Neuralyst is an add-in
package for Excel. Approx. price: $195 for windows or
Mac. Comments: A simple model that is easy to use.
Integrates nicely into Microsoft Excel. Allows user to
create, train, and run backprop ANN models entirely
within an Excel spreadsheet. Provides macro functions that
can be called from Excel macro's, allowing you to build a
custom Window's interface using Excel's macro language
and Visual Basic tools. The new version 1.4 includes a
genetic algorithm to guide the training process. A good
bargain to boot. (Comments by Duane Highley, a user and
NOT the program developer.
dhighley@ozarks.sgcl.lib.mo.us)
12. NeuFuz4
+++++++++++
Name: NeuFuz4 Company: National Semiconductor
Corporation Address: 2900 Semiconductor Drive, Santa
Clara, CA, 95052, or: Industriestrasse 10, D-8080
Fuerstenfeldbruck, Germany, or: Sumitomo Chemical
Engineering Center, Bldg. 7F 1-7-1, Nakase, Mihama-Ku,
Chiba-City, Ciba Prefecture 261, JAPAN, or: 15th Floor,
Straight Block, Ocean Centre, 5 Canton Road, Tsim Sha
Tsui East, Kowloon, Hong Kong, Phone: (800) 272-9959
(Americas), : 011-49-8141-103-0 Germany :
0l1-81-3-3299-7001 Japan : (852) 737-1600 Hong Kong
Email: neufuz@esd.nsc.com (Neural net inquiries only)
URL:
http://www.commerce.net/directories/participants/ns/home.html
Basic capabilities: Uses backpropagation techniques to
initially select fuzzy rules and membership functions. The
result is a fuzzy associative memory (FAM) which
implements an approximation of the training data.
Operating Systems: 486DX-25 or higher with math
co-processor DOS 5.0 or higher with Windows 3.1, mouse,
VGA or better, minimum 4 MB RAM, and parallel port.
Approx. price : depends on version - see below. Comments
: Not for the serious Neural Network researcher, but good
for a person who has little understanding of Neural Nets -
and wants to keep it that way. The systems are aimed at
low end controls applications in automotive, industrial, and
appliance areas. NeuFuz is a neural-fuzzy technology
which uses backpropagation techniques to initially select
fuzzy rules and membership functions. Initial stages of
design using NeuFuz technology are performed using
training data and backpropagation. The result is a fuzzy
associative memory (FAM) which implements an
approximation of the training data. By implementing a
FAM, rather than a multi-layer perceptron, the designer
has a solution which can be understood and tuned to a
particular application using Fuzzy Logic design techniques.
There are several different versions, some with COP8 Code
Generator (COP8 is National's family of 8-bit
microcontrollers) and COP8 in-circuit emulator (debug
module).
13. Cortex-Pro
++++++++++++++
Cortex-Pro information is on WWW at:
http://www.neuronet.ph.kcl.ac.uk/neuronet/software/cortex/www1.html.
You can download a working demo from there. Contact:
Michael Reiss (
http://www.mth.kcl.ac.uk/~mreiss/mick.html) email:
<m.reiss@kcl.ac.uk>.
14. PARTEK
++++++++++
PARTEK is a powerful, integrated environment for visual
and quantitative data analysis and pattern recognition.
Drawing from a wide variety of disciplines including
Artificial Neural Networks, Fuzzy Logic, Genetic
Algorithms, and Statistics, PARTEK integrates data
analysis and modeling tools into an easy to use "point and
click" system. The following modules are available from
PARTEK; functions from different modules are integrated
with each other whereever possible:
1. The PARTEK/AVB - The Analytical/Visual Base.
(TM)
* Analytical Spreadsheet (TM)
The Analytical Spreadsheet is a powerful and easy to use data analysis,
transformations, and visualization tool. Some features include:
- import native format ascii/binary data
- recognition and resolution of missing data
- complete set of common mathematical & statistical functions
- contingency table analysis / correspondence analysis
- univariate histogram analysis
- extensive set of smoothing and normalization transformations
- easily and quickly plot color-coded 1-D curves and histograms,
2-D, 3-D, and N-D mapped scatterplots, highlighting selected
patterns
- Command Line (Tcl) and Graphical Interface
* Pattern Visualization System (TM)
The Pattern Visualization System offers the most powerful tools for
visual analysis of the patterns in your data. Some features include:
- automatically maps N-D data down to 3-D for visualization of
*all* of your variables at once
- hard copy color Postscript output
- a variety of color-coding, highlighting, and labeling options
allow you to generate meaningful graphics
* Data Filters
Filter out selected rows and/or columns of your data for flexible and
efficient cross-validation, jackknifing, bootstrapping, feature set
evaluation, and more.
* Random # Generators
Generate random numbers from any of the following parameterized
distributions:
- uniform, normal, exponential, gamma, binomial, poisson
* Many distance/similarity metrics
Choose the appropriate distance metric for your data:
- euclidean, mahalanobis, minkowski, maximum value, absolute value,
shape coefficient, cosine coefficient, pearson correlation,
rank correlation, kendall's tau, canberra, and bray-curtis
* Tcl/Tk command line interface
2. The PARTEK/DSA - Data Structure Analysis
Module
* Principal Components Analysis and Regression
Also known as Eigenvector Projection or Karhunen-Loeve Expansions,
PCA removes redundant information from your data.
- component analysis, correlate PC's with original variables
- choice of covariance, correlation, or product dispersion matrices
- choice of eigenvector, y-score, and z-score projections
- view SCREE and log-eigenvalue plots
* Cluster Analysis
Does the data form groups? How many? How compact? Cluster Analysis
is the tool to answer these questions.
- choose between several distance metrics
- optionally weight individual patterns
- manually or auto-select the cluster number and initial centers
- dump cluster counts, mean, cluster to cluster distances,
cluster variances, and cluster labeled data to a matrix viewer or
the Analytical Spreadsheet for further analysis
- visualize n-dimensional clustering
- assess goodness of partion using several internal and external
criteria metrics
* N-Dimensional Histogram Analysis
Among the most inportant questions a researcher needs to know when
analyzing patterns is whether or not the patterns can distinguish
different classes of data. N-D Histogram Analysis is one tool to
answer this question.
- measures histogram overlap in n-dimensional space
- automatically find the best subset of features
- rank the overlap of your best feature combinations
* Non-Linear Mapping
NLM is an iterative algorithm for visually analyzing the structure of
n-dimensional data. NLM produces a non-linear mapping of data which
preserves interpoint distances of n-dimensional data while reducing
to a lower dimensionality - thus preserving the structure of the data.
- visually analyze structure of n-dimensional data
- track progress with error curves
- orthogonal, PCA, and random initialization
3. The PARTEK/CP - Classification and Prediction
Module
* Multi-Layer Perceptron
The most popular among the neural pattern recognition tools is the MLP.
PARTEK takes the MLP to a new dimension, by allowing the network to
learn by adapting ALL of its parameters to solve a problem.
- adapts output bias, neuron activation steepness, and neuron
dynamic range, as well as weights and input biases
- auto-scaling at input and output - no need to rescale your data
- choose between sigmoid, gaussian, linear, or mixture of neurons
- learning rate, momentum can be set independently for each parameter
- variety of learning methods and network initializations
- view color-coded network, error, etc as network trains, tests, runs
* Learning Vector Quantization
Because LVQ is a multiple prototype classifier, it adapts to identify
multiple sub-groups within classes
- LVQ1, LVQ2, and LVQ3 training methods
- 3 different functions for adapting learning rate
- choose between several distance metrics
- fuzzy and crisp classifications
- set number of prototypes individually for each class
* Bayesian Classifier
Bayes methods are the statistical decision theory approach to
classification. This classifier uses statistical properties of your
data to develop a classification model.
PARTEK is available on HP, IBM, Silicon Graphics, and
SUN workstations. For more information, send email to
"info@partek.com" or call (314)926-2329.
15. NeuroSolutions v2.0
+++++++++++++++++++++++
NeuroSolutions is a graphical neural network simulation tool. It is
the only commercial Windows package to support trajectory learning
with backpropagation through time. Because of its object-oriented
design, NeuroSolutions provides the flexibility needed to construct
a wide range of learning paradigms and network topologies. Its GUI
and extensive probing ability streamline the experimentation process
by providing real-time analysis of the network during learning.
Construct any neural network belonging to the additive model,
including locally and globally recurrent systems. Use a variety of
unsupervised learning procedures, such as Hebbian, Sanger's, Oja's,
Competitive and Kohonen. Implement RBF, PCA, counterpropagation and
other hybrid network topologies by seamlessly integrating
supervised and unsupervised learning.
During learning, animate changes of internal variables such as
activations, weights, sensitivities and gradients with a variety of
probes. Examples are the oscilloscope, spectrum analyzer, 3D state
space, scatter, 3D surface, matrix and bitmap.
NeuroSolutions' NeuralWizard utility automates the neural network
design process. Choose between a wide range of neural models. The
network parameters are dynamically adjusted based on the user's
training data. It is a powerful tool used by both beginners and
researchers alike.
NeuroSolutions offers advanced features to meet the integration
needs of neural network developers. Once a system is designed and
simulated using the icon-based development environment,
NeuroSolutions will generate ANSI-compatible C++ source code to be
compiled and linked into custom applications. NeuroSolutions can
also be customized through user-defined DLL's and OLE support.
Price: $195 - $1995
Demo copy available from company or by anonymous ftp:
ftp://oak.oakland.edu/SimTel/win3/neurlnet/ns2demo1.zip
ftp://oak.oakland.edu/SimTel/win3/neurlnet/ns2demo2.zip
NeuroDimension, Inc.
720 S.W. 2nd Ave., Suite 458
Gainesville FL, 32601
Phone: (800) 634-3327 or
(904) 377-5144
FAX: (904) 338-6779
Email: info@nd.com
------------------------------------------------------------------------
19. A: Neural Network hardware?
===============================
[who will write some short comment on the most important
HW-packages and chips?]
The Number 1 of each volume of the journal "Neural Networks"
has a list of some dozens of suppliers of Neural Network support:
Software, Hardware, Support, Programming, Design and Service.
There is an article by J.A. Hegt (Eindhoven) from 1993 available
from ftp://ftp.urc.tue.nl/pub/neural/hardware_general.ps.gz
Here is a short list of companies:
1. HNC, INC.
++++++++++++
5501 Oberlin Drive
San Diego
California 92121
(619) 546-8877
and a second address at
7799 Leesburg Pike, Suite 900
Falls Church, Virginia
22043
(703) 847-6808
Note: Australian Dist.: Unitronics
Tel : (09) 4701443
Contact: Martin Keye
HNC markets:
'Image Document Entry Processing Terminal' - it recognises
handwritten documents and converts the info to ASCII.
'ExploreNet 3000' - a NN demonstrator
'Anza/DP Plus'- a Neural Net board with 25MFlop or 12.5M peak
interconnects per second.
2. SAIC (Sience Application International Corporation)
++++++++++++++++++++++++++++++++++++++++++++++++++++++
10260 Campus Point Drive
MS 71, San Diego
CA 92121
(619) 546 6148
Fax: (619) 546 6736
3. Micro Devices
++++++++++++++++
30 Skyline Drive
Lake Mary
FL 32746-6201
(407) 333-4379
MicroDevices makes MD1220 - 'Neural Bit Slice'
Each of the products mentioned sofar have very different usages.
Although this sounds similar to Intel's product, the
architectures are not.
4. Intel Corp
+++++++++++++
2250 Mission College Blvd
Santa Clara, Ca 95052-8125
Attn ETANN, Mail Stop SC9-40
(408) 765-9235
Intel is making an experimental chip:
80170NW - Electrically trainable Analog Neural Network (ETANN)
It has 64 'neurons' on it - almost fully internally connectted
and the chip can be put in an hierarchial architecture to do 2 Billion
interconnects per second.
Support software by
California Scientific Software
10141 Evening Star Dr #6
Grass Valley, CA 95945-9051
(916) 477-7481
Their product is called 'BrainMaker'.
5. NeuralWare, Inc
++++++++++++++++++
Penn Center West
Bldg IV Suite 227
Pittsburgh
PA 15276
They only sell software/simulator but for many platforms.
6. Tubb Research Limited
++++++++++++++++++++++++
7a Lavant Street
Peterfield
Hampshire
GU32 2EL
United Kingdom
Tel: +44 730 60256
7. Adaptive Solutions Inc
+++++++++++++++++++++++++
1400 NW Compton Drive
Suite 340
Beaverton, OR 97006
U. S. A.
Tel: 503-690-1236; FAX: 503-690-1249
8. NeuroDynamX, Inc.
++++++++++++++++++++
4730 Walnut St., Suite 101B
Boulder, CO 80301
Voice: (303) 442-3539 Fax: (303) 442-2854
Internet: techsupport@ndx.com
NDX sells a number neural network hardware products:
NDX Neural Accelerators: a line of i860-based accelerator cards for
the PC that give up to 45 million connections per second for use
with the DynaMind neural network software.
iNNTS: Intel's 80170NX (ETANN) Neural Network Training System. NDX's president
was one of the co-designers of this chip.
9. IC Tech
++++++++++
NEURO-COMPUTING IC's:
* DANN050L (dendro-dendritic artificial neural network)
+ 50 neurons fully connected at the input
+ on-chip digital learning capability
+ 6 billion connections/sec peak speed
+ learns 7 x 7 template in < 50 nsec., recalls in < 400 nsec.
+ low power < 100 milli Watts
+ 64-pin package
* NCA717D (neuro correlator array)
+ analog template matching in < 500 nsec.
+ analog input / digital output pins for real-time computation
+ vision applications in stereo and motion computation
+ 40-pin package
NEURO COMPUTING BOARD:
* ICT1050
+ IBM PC compatible or higher
+ with on-board DANN050L
+ digital interface
+ custom configurations available
Contact:
IC Tech (Innovative Computing Technologies, Inc.)
4138 Luff Court
Okemos, MI 48864
(517) 349-4544
ictech@mcimail.com
And here is an incomplete overview over known Neural
Computers with their newest known reference.
\subsection*{Digital}
\subsubsection{Special Computers}
{\bf AAP-2}
Takumi Watanabe, Yoshi Sugiyama, Toshio Kondo, and Yoshihiro Kitamura.
Neural network simulation on a massively parallel cellular array
processor: AAP-2.
In International Joint Conference on Neural Networks, 1989.
{\bf ANNA}
B.E.Boser, E.Sackinger, J.Bromley, Y.leChun, and L.D.Jackel.\\
Hardware Requirements for Neural Network Pattern Classifiers.\\
In {\it IEEE Micro}, 12(1), pages 32-40, February 1992.
{\bf Analog Neural Computer}
Paul Mueller et al.
Design and performance of a prototype analog neural computer.
In Neurocomputing, 4(6):311-323, 1992.
{\bf APx -- Array Processor Accelerator}\\
F.Pazienti.\\
Neural networks simulation with array processors.
In {\it Advanced Computer Technology, Reliable Systems and Applications;
Proceedings of the 5th Annual Computer Conference}, pages 547-551.
IEEE Comput. Soc. Press, May 1991. ISBN: 0-8186-2141-9.
{\bf ASP -- Associative String Processor}\\
A.Krikelis.\\
A novel massively associative processing architecture for the
implementation artificial neural networks.\\
In {\it 1991 International Conference on Acoustics, Speech and
Signal Processing}, volume 2, pages 1057-1060. IEEE Comput. Soc. Press,
May 1991.
{\bf BSP400}
Jan N.H. Heemskerk, Jacob M.J. Murre, Jaap Hoekstra, Leon H.J.G.
Kemna, and Patrick T.W. Hudson.
The bsp400: A modular neurocomputer assembled from 400 low-cost
microprocessors.
In International Conference on Artificial Neural Networks. Elsevier
Science, 1991.
{\bf BLAST}\\
J.G.Elias, M.D.Fisher, and C.M.Monemi.\\
A multiprocessor machine for large-scale neural network simulation.
In {\it IJCNN91-Seattle: International Joint Conference on Neural
Networks}, volume 1, pages 469-474. IEEE Comput. Soc. Press, July 1991.
ISBN: 0-7883-0164-1.
{\bf CNAPS Neurocomputer}\\
H.McCartor\\
Back Propagation Implementation on the Adaptive Solutions CNAPS
Neurocomputer.\\
In {\it Advances in Neural Information Processing Systems}, 3, 1991.
{\bf GENES~IV and MANTRA~I}\\
Paolo Ienne and Marc A. Viredaz\\
{GENES~IV}: A Bit-Serial Processing Element for a Multi-Model
Neural-Network Accelerator\\
Proceedings of the International Conference on Application Specific Array
Processors, Venezia, 1993.
{\bf MA16 -- Neural Signal Processor}
U.Ramacher, J.Beichter, and N.Bruls.\\
Architecture of a general-purpose neural signal processor.\\
In {\it IJCNN91-Seattle: International Joint Conference on Neural
Networks}, volume 1, pages 443-446. IEEE Comput. Soc. Press, July 1991.
ISBN: 0-7083-0164-1.
{\bf MANTRA I}\\
Marc A. Viredaz\\
{MANTRA~I}: An {SIMD} Processor Array for Neural Computation
Proceedings of the Euro-ARCH'93 Conference, {M\"unchen}, 1993.
{\bf Mindshape}
Jan N.H. Heemskerk, Jacob M.J. Murre Arend Melissant, Mirko Pelgrom,
and Patrick T.W. Hudson.
Mindshape: a neurocomputer concept based on a fractal architecture.
In International Conference on Artificial Neural Networks. Elsevier
Science, 1992.
{\bf mod 2}
Michael L. Mumford, David K. Andes, and Lynn R. Kern.
The mod 2 neurocomputer system design.
In IEEE Transactions on Neural Networks, 3(3):423-433, 1992.
{\bf NERV}\\
R.Hauser, H.Horner, R. Maenner, and M.Makhaniok.\\
Architectural Considerations for NERV - a General Purpose Neural
Network Simulation System.\\
In {\it Workshop on Parallel Processing: Logic, Organization and
Technology -- WOPPLOT 89}, pages 183-195. Springer Verlag, Mars 1989.
ISBN: 3-5405-5027-5.
{\bf NP -- Neural Processor}\\
D.A.Orrey, D.J.Myers, and J.M.Vincent.\\
A high performance digital processor for implementing large artificial
neural networks.\\
In {\it Proceedings of of the IEEE 1991 Custom Integrated Circuits
Conference}, pages 16.3/1-4. IEEE Comput. Soc. Press, May 1991.
ISBN: 0-7883-0015-7.
{\bf RAP -- Ring Array Processor }\\
N.Morgan, J.Beck, P.Kohn, J.Bilmes, E.Allman, and J.Beer.\\
The ring array processor: A multiprocessing peripheral for connectionist
applications. \\
In {\it Journal of Parallel and Distributed Computing}, pages
248-259, April 1992.
{\bf RENNS -- REconfigurable Neural Networks Server}\\
O.Landsverk, J.Greipsland, J.A.Mathisen, J.G.Solheim, and L.Utne.\\
RENNS - a Reconfigurable Computer System for Simulating Artificial
Neural Network Algorithms.\\
In {\it Parallel and Distributed Computing Systems, Proceedings of the
ISMM 5th International Conference}, pages 251-256. The International
Society for Mini and Microcomputers - ISMM, October 1992.
ISBN: 1-8808-4302-1.
{\bf SMART -- Sparse Matrix Adaptive and Recursive Transforms}\\
P.Bessiere, A.Chams, A.Guerin, J.Herault, C.Jutten, and J.C.Lawson.\\
From Hardware to Software: Designing a ``Neurostation''.\\
In {\it VLSI design of Neural Networks}, pages 311-335, June 1990.
{\bf SNAP -- Scalable Neurocomputer Array Processor}
E.Wojciechowski.\\
SNAP: A parallel processor for implementing real time neural networks.\\
In {\it Proceedings of the IEEE 1991 National Aerospace and Electronics
Conference; NAECON-91}, volume 2, pages 736-742. IEEE Comput.Soc.Press,
May 1991.
{\bf Toroidal Neural Network Processor}\\
S.Jones, K.Sammut, C.Nielsen, and J.Staunstrup.\\
Toroidal Neural Network: Architecture and Processor Granularity
Issues.\\
In {\it VLSI design of Neural Networks}, pages 229-254, June 1990.
{\bf SMART and SuperNode}
P. Bessi`ere, A. Chams, and P. Chol.
MENTAL : A virtual machine approach to artificial neural networks
programming. In NERVES, ESPRIT B.R.A. project no 3049, 1991.
\subsubsection{Standard Computers}
{\bf EMMA-2}\\
R.Battiti, L.M.Briano, R.Cecinati, A.M.Colla, and P.Guido.\\
An application oriented development environment for Neural Net models on
multiprocessor Emma-2.\\
In {\it Silicon Architectures for Neural Nets; Proceedings for the IFIP
WG.10.5 Workshop}, pages 31-43. North Holland, November 1991.
ISBN: 0-4448-9113-7.
{\bf iPSC/860 Hypercube}\\
D.Jackson, and D.Hammerstrom\\
Distributing Back Propagation Networks Over the Intel iPSC/860
Hypercube}\\
In {\it IJCNN91-Seattle: International Joint Conference on Neural
Networks}, volume 1, pages 569-574. IEEE Comput. Soc. Press, July 1991.
ISBN: 0-7083-0164-1.
{\bf SCAP -- Systolic/Cellular Array Processor}\\
Wei-Ling L., V.K.Prasanna, and K.W.Przytula.\\
Algorithmic Mapping of Neural Network Models onto Parallel SIMD
Machines.\\
In {\it IEEE Transactions on Computers}, 40(12), pages 1390-1401,
December 1991. ISSN: 0018-9340.
------------------------------------------------------------------------
20. A: Databases for experimentation with NNs?
==============================================
1. The neural-bench Benchmark collection
++++++++++++++++++++++++++++++++++++++++
Accessible via anonymous FTP on ftp.cs.cmu.edu
[128.2.206.173] in directory /afs/cs/project/connect/bench. In
case of problems or if you want to donate data, email
contact is "neural-bench@cs.cmu.edu". The data sets in
this repository include the 'nettalk' data, 'two spirals',
protein structure prediction, vowel recognition, sonar signal
classification, and a few others.
2. Proben1
++++++++++
Proben1 is a collection of 12 learning problems consisting
of real data. The datafiles all share a single simple common
format. Along with the data comes a technical report
describing a set of rules and conventions for performing
and reporting benchmark tests and their results. Accessible
via anonymous FTP on ftp.cs.cmu.edu [128.2.206.173] as
/afs/cs/project/connect/bench/contrib/prechelt/proben1.tar.gz.
and also on ftp.ira.uka.de [129.13.10.90] as
/pub/neuron/proben.tar.gz. The file is about 1.8 MB and
unpacks into about 20 MB.
3. UCI machine learning database
++++++++++++++++++++++++++++++++
Accessible via anonymous FTP on ics.uci.edu [128.195.1.1]
in directory /pub/machine-learning-databases".
4. NIST special databases of the National Institute Of
++++++++++++++++++++++++++++++++++++++++++++++++++++++
Standards And Technology:
+++++++++++++++++++++++++
Several large databases, each delivered on a CD-ROM.
Here is a quick list.
o NIST Binary Images of Printed Digits, Alphas, and
Text
o NIST Structured Forms Reference Set of Binary
Images
o NIST Binary Images of Handwritten Segmented
Characters
o NIST 8-bit Gray Scale Images of Fingerprint Image
Groups
o NIST Structured Forms Reference Set 2 of Binary
Images
o NIST Test Data 1: Binary Images of Hand-Printed
Segmented Characters
o NIST Machine-Print Database of Gray Scale and
Binary Images
o NIST 8-Bit Gray Scale Images of Mated
Fingerprint Card Pairs
o NIST Supplemental Fingerprint Card Data (SFCD)
for NIST Special Database 9
o NIST Binary Image Databases of Census Miniforms
(MFDB)
o NIST Mated Fingerprint Card Pairs 2 (MFCP 2)
o NIST Scoring Package Release 1.0
o NIST FORM-BASED HANDPRINT
RECOGNITION SYSTEM
Here are example descriptions of two of these databases:
NIST special database 2: Structured Forms Reference Set
-------------------------------------------------------
(SFRS)
------
The NIST database of structured forms contains 5,590 full
page images of simulated tax forms completed using
machine print. THERE IS NO REAL TAX DATA IN
THIS DATABASE. The structured forms used in this
database are 12 different forms from the 1988, IRS 1040
Package X. These include Forms 1040, 2106, 2441, 4562,
and 6251 together with Schedules A, B, C, D, E, F and SE.
Eight of these forms contain two pages or form faces
making a total of 20 form faces represented in the database.
Each image is stored in bi-level black and white raster
format. The images in this database appear to be real forms
prepared by individuals but the images have been
automatically derived and synthesized using a computer
and contain no "real" tax data. The entry field values on
the forms have been automatically generated by a
computer in order to make the data available without the
danger of distributing privileged tax information. In
addition to the images the database includes 5,590 answer
files, one for each image. Each answer file contains an
ASCII representation of the data found in the entry fields
on the corresponding image. Image format documentation
and example software are also provided. The uncompressed
database totals approximately 5.9 gigabytes of data.
NIST special database 3: Binary Images of Handwritten
-----------------------------------------------------
Segmented Characters (HWSC)
---------------------------
Contains 313,389 isolated character images segmented
from the 2,100 full-page images distributed with "NIST
Special Database 1". 223,125 digits, 44,951 upper-case, and
45,313 lower-case character images. Each character image
has been centered in a separate 128 by 128 pixel region,
error rate of the segmentation and assigned classification is
less than 0.1%. The uncompressed database totals
approximately 2.75 gigabytes of image data and includes
image format documentation and example software.
The system requirements for all databases are a 5.25"
CD-ROM drive with software to read ISO-9660 format.
Contact: Darrin L. Dimmick; dld@magi.ncsl.nist.gov;
(301)975-4147
The prices of the databases are between US$ 250 and 1895
If you wish to order a database, please contact: Standard
Reference Data; National Institute of Standards and
Technology; 221/A323; Gaithersburg, MD 20899; Phone:
(301)975-2208; FAX: (301)926-0416
Samples of the data can be found by ftp on
sequoyah.ncsl.nist.gov in directory /pub/data A more
complete description of the available databases can be
obtained from the same host as /pub/databases/catalog.txt
5. CEDAR CD-ROM 1: Database of Handwritten Cities,
++++++++++++++++++++++++++++++++++++++++++++++++++
States, ZIP Codes, Digits, and Alphabetic Characters
++++++++++++++++++++++++++++++++++++++++++++++++++++
The Center Of Excellence for Document Analysis and
Recognition (CEDAR) State University of New York at
Buffalo announces the availability of CEDAR CDROM 1:
USPS Office of Advanced Technology The database
contains handwritten words and ZIP Codes in high
resolution grayscale (300 ppi 8-bit) as well as binary
handwritten digits and alphabetic characters (300 ppi
1-bit). This database is intended to encourage research in
off-line handwriting recognition by providing access to
handwriting samples digitized from envelopes in a working
post office.
Specifications of the database include:
+ 300 ppi 8-bit grayscale handwritten words (cities,
states, ZIP Codes)
o 5632 city words
o 4938 state words
o 9454 ZIP Codes
+ 300 ppi binary handwritten characters and digits:
o 27,837 mixed alphas and numerics segmented
from address blocks
o 21,179 digits segmented from ZIP Codes
+ every image supplied with a manually determined
truth value
+ extracted from live mail in a working U.S. Post
Office
+ word images in the test set supplied with dic-
tionaries of postal words that simulate partial
recognition of the corresponding ZIP Code.
+ digit images included in test set that simulate
automatic ZIP Code segmentation. Results on these
data can be projected to overall ZIP Code recogni-
tion performance.
+ image format documentation and software included
System requirements are a 5.25" CD-ROM drive with
software to read ISO-9660 format. For any further
information, including how to order the database, please
contact: Jonathan J. Hull, Associate Director, CEDAR, 226
Bell Hall State University of New York at Buffalo,
Buffalo, NY 14260; hull@cs.buffalo.edu (email)
6. AI-CD-ROM (see under answer 13)
++++++++++++++++++++++++++++++++++
7. Time series archive
++++++++++++++++++++++
Various datasets of time series (to be used for prediction
learning problems) are available for anonymous ftp from
ftp.santafe.edu [192.12.12.1] in /pub/Time-Series".
Problems are for example: fluctuations in a far-infrared
laser; Physiological data of patients with sleep apnea; High
frequency currency exchange rate data; Intensity of a white
dwarf star; J.S. Bachs final (unfinished) fugue from "Die
Kunst der Fuge"
Some of the datasets were used in a prediction contest and
are described in detail in the book "Time series prediction:
Forecasting the future and understanding the past", edited
by Weigend/Gershenfield, Proceedings Volume XV in the
Santa Fe Institute Studies in the Sciences of Complexity
series of Addison Wesley (1994).
------------------------------------------------------------------------
That's all folks.
Acknowledgements: Thanks to all the people who helped to get the stuff
above into the posting. I cannot name them all, because
I would make far too many errors then. :->
No? Not good? You want individual credit?
OK, OK. I'll try to name them all. But: no guarantee....
THANKS FOR HELP TO:
(in alphabetical order of email adresses, I hope)
o Steve Ward <71561.2370@CompuServe.COM>
o Allen Bonde <ab04@harvey.gte.com>
o Accel Infotech Spore Pte Ltd <accel@solomon.technet.sg>
o Ales Krajnc <akrajnc@fagg.uni-lj.si>
o Alexander Linden <al@jargon.gmd.de>
o Matthew David Aldous <aldous@mundil.cs.mu.OZ.AU>
o S.Taimi Ames <ames@reed.edu>
o Axel Mulder <amulder@move.kines.sfu.ca>
o anderson@atc.boeing.com
o Andy Gillanders <andy@grace.demon.co.uk>
o Davide Anguita <anguita@ICSI.Berkeley.EDU>
o Avraam Pouliakis <apou@leon.nrcps.ariadne-t.gr>
o Kim L. Blackwell <avrama@helix.nih.gov>
o Mohammad Bahrami <bahrami@cse.unsw.edu.au>
o Paul Bakker <bakker@cs.uq.oz.au>
o Stefan Bergdoll <bergdoll@zxd.basf-ag.de>
o Jamshed Bharucha <bharucha@casbs.Stanford.EDU>
o Yijun Cai <caiy@mercury.cs.uregina.ca>
o L. Leon Campbell <campbell@brahms.udel.edu>
o Craig Watson <craig@magi.ncsl.nist.gov>
o Yaron Danon <danony@goya.its.rpi.edu>
o David Ewing <dave@ndx.com>
o David DeMers <demers@cs.ucsd.edu>
o Denni Rognvaldsson <denni@thep.lu.se>
o Duane Highley <dhighley@ozarks.sgcl.lib.mo.us>
o Dick.Keene@Central.Sun.COM
o DJ Meyer <djm@partek.com>
o Donald Tveter <drt@mcs.com>
o Athanasios Episcopos
<EPISCOPO@icarus.som.clarkson.edu>
o Frank Schnorrenberg <fs0997@easttexas.tamu.edu>
o Gary Lawrence Murphy <garym@maya.isis.org>
o gaudiano@park.bu.edu
o Lee Giles <giles@research.nj.nec.com>
o Glen Clark <opto!glen@gatech.edu>
o Phil Goodman <goodman@unr.edu>
o guy@minster.york.ac.uk
o Joerg Heitkoetter
<heitkoet@lusty.informatik.uni-dortmund.de>
o Ralf Hohenstein <hohenst@math.uni-muenster.de>
o Gamze Erten <ictech@mcimail.com>
o Ed Rosenfeld <IER@aol.com>
o Jean-Denis Muller <jdmuller@vnet.ibm.com>
o Jeff Harpster <uu0979!jeff@uu9.psi.com>
o Jonathan Kamens <jik@MIT.Edu>
o J.J. Merelo <jmerelo@kal-el.ugr.es>
o Jon Gunnar Solheim <jon@kongle.idt.unit.no>
o Josef Nelissen <jonas@beor.informatik.rwth-aachen.de>
o Joey Rogers <jrogers@buster.eng.ua.edu>
o Subhash Kak <kak@gate.ee.lsu.edu>
o Ken Karnofsky <karnofsky@mathworks.com>
o Kjetil.Noervaag@idt.unit.no
o Luke Koops <koops@gaul.csd.uwo.ca>
o William Mackeown <mackeown@compsci.bristol.ac.uk>
o Mark Plumbley <mark@dcs.kcl.ac.uk>
o Peter Marvit <marvit@cattell.psych.upenn.edu>
o masud@worldbank.org
o Yoshiro Miyata <miyata@sccs.chukyo-u.ac.jp>
o Madhav Moganti <mmogati@cs.umr.edu>
o Jyrki Alakuijala <more@ee.oulu.fi>
o Michael Reiss <m.reiss@kcl.ac.uk>
o mrs@kithrup.com
o Maciek Sitnik <msitnik@plearn.edu.pl>
o R. Steven Rainwater <ncc@ncc.jvnc.net>
o Paolo Ienne <Paolo.Ienne@di.epfl.ch>
o Paul Keller <pe_keller@ccmail.pnl.gov>
o Michael Plonski <plonski@aero.org>
o Lutz Prechelt <prechelt@ira.uka.de> [creator of FAQ]
o Richard Andrew Miles Outerbridge
<ramo@uvphys.phys.uvic.ca>
o Robin L. Getz <rgetz@esd.nsc.com>
o Richard Cornelius <richc@rsf.atd.ucar.edu>
o Rob Cunningham <rkc@xn.ll.mit.edu>
o Robert.Kocjancic@IJS.si
o Stefan Vogt <s_vogt@cis.umassd.edu>
o Osamu Saito <saito@nttica.ntt.jp>
o Warren Sarle <saswss@unx.sas.com>
o Scott Fahlman <sef+@cs.cmu.edu>
o <seibert@ll.mit.edu>
o Sheryl Cormicle <sherylc@umich.edu>
o Ted Stockwell <ted@aps1.spa.umn.edu>
o Serge Waterschoot <swater@minf.vub.ac.be>
o Thomas G. Dietterich <tgd@research.cs.orst.edu>
o Thomas.Vogel@cl.cam.ac.uk
o Ulrich Wendl <uli@unido.informatik.uni-dortmund.de>
o M. Verleysen <verleysen@dice.ucl.ac.be>
o Sherif Hashem <vg197@neutrino.pnl.gov>
o Matthew P Wiener <weemba@sagi.wistar.upenn.edu>
o Wesley Elsberry <welsberr@orca.tamu.edu>
Bye
Lutz
Neural network FAQ / Lutz Prechelt, prechelt@ira.uka.de
--
Lutz Prechelt (http://wwwipd.ira.uka.de/~prechelt/) | Whenever you
Institut fuer Programmstrukturen und Datenorganisation | complicate things,
Universitaet Karlsruhe; 76128 Karlsruhe; Germany | they get
(Voice: +49/721/608-4068, FAX: +49/721/694092) | less simple.