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Subject: FAQ in comp.ai.neural-nets -- monthly posting
Newsgroups: comp.ai.neural-nets,comp.answers,news.answers
From: prechelt@ira.uka.de (Lutz Prechelt)
Date: 28 Oct 1994 03:17:40 GMT
Archive-name: neural-net-faq
Last-modified: 1994/10/25
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/Tichy/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?
6. How many learning methods for NNs exist? Which?
7. What about Genetic Algorithms?
8. What about Fuzzy Logic?
9. Good introductory literature about Neural Networks?
10. Any journals and magazines about Neural Networks?
11. The most important conferences concerned with Neural
Networks?
12. Neural Network Associations?
13. Other sources of information about NNs?
14. Freely available software packages for NN simulation?
15. Commercial software packages for NN simulation?
16. Neural Network hardware?
17. 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 inteded 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 ansers 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?
================================
It 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 9).
------------------------------------------------------------------------
6. 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 distiction 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)
------------------------------------------------------------------------
7. 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.
------------------------------------------------------------------------
8. 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.
------------------------------------------------------------------------
o A: Good introductory literature about Neural
o ============================================
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."
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."
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."
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".
------------------------------------------------------------------------
o A: Any journals and magazines about Neural
o ==========================================
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., 687 Hartwell Street, Teaneck,
NJ 07666. Tel: (201) 837-8858; Eurpoe: World Scientific Publishing
Co. Pte. Ltd., 73 Lynton Mead, Totteridge, London N20-8DH, England.
Tel: (01) 4462461; Other: World Scientific Publishing Co. Pte. Ltd.,
Farrer Road, P.O. Box 128, Singapore 9128. Tel: 2786188
Freq.: Quarterly (Vol. 1 in 1990?)
Cost/Yr: Individual $42, Institution $88 (plus $9-$17 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.
It publishes original contributions on all aspects of this
broad subject which involves physics, biology, psychology, computer
science and engineering. Contributions include research papers,
reviews and short communications. The journal presents a fresh
undogmatic attitude towards this multidisciplinary field with the
aim to be a forum for novel ideas and improved understanding of
collective and cooperative phenomena with computational
capabilities.
(Note: Remarks supplied by B. Lautrup (editor),
"LAUTRUP%nbivax.nbi.dk@CUNYVM.CUNY.EDU" )
Review is reported to be very slow.
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: Commericial 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: Concepts in NeuroScience
Publish: World Scientific Publishing
Address: Same Address (?) as for International Journal of Neural Systems
Freq.: Twice per year (vol. 1 in 1989)
Remark: Mainly Review Articles(?)
(Note: remarks by Osamu Saito "saito@nttica.NTT.JP")
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: World Scientific Publ. Co.
Farrer Rd. P.O.Box 128, Singapore 9128
or: 687 Hartwell St., Teaneck, N.J. 07666 U.S.A
or: 73 Lynton Mead, Totteridge, London N20 8DH, England
Freq: published quarterly
Eds: G. Fox, H. Herrmann and K. Kaneko
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.
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)
------------------------------------------------------------------------
o A: The most important conferences concerned with
o ================================================
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. Annual Conference of the Cognitive Science Society
4. [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
------------------------------------------------------------------------
o A: Neural Network Associations?
o ===============================
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
------------------------------------------------------------------------
o A: Other sources of information about NNs?
o ==========================================
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: 409-589-3338; Sysop: Wesley R. Elsberry; P.O. Box
4201, College Station, TX 77843; 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. World Wide Web
+++++++++++++++++
In World-Wide-Web (WWW, for example via the xmosaic
program) you can read neural network information by opening
one of the following universal 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.msrc.pnl.gov:2080/docs/cie/neural/neural.homepage.html
(Richland, Washington),
http://www.cosy.sbg.ac.at/~rschwaig/rschwaig/projects.html
(Salzburg, Austria),
http://http2.sils.umich.edu/Public/nirg/nirg1.html (Michigan).
8. 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
9. 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
------------------------------------------------------------------------
o A: Freely available software packages for NN
o ============================================
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
[192.5.53.209] in directory pub/simulator as the files README (8
KB), rcs_v4.2.justdoc.tar.Z (1.6 MB, Documentation),
rcs_v4.2.justsrc.tar.Z (1.4 MB, Source code),
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 1.4.1 (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 from
genesis.cns.caltech.edu [131.215.137.64]. Use 'telnet' to
genesis.cns.caltech.edu beforehands and login as the user "genesis"
(no password required). If you answer all the questions asked of
you an 'ftp' account will automatically be created for you. You
can then 'ftp' back to the machine and download the software (ca.
3 MB). Contact: genesis@cns.caltech.edu.
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 not
available and undergoing a major reconstruction (April 94).
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 [129.26.8.90] 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 to be released in Spring of
1994 -- some of the 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.
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 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 or .
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 [131.246.9.95] 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.technion.ac.il [132.68.1.10] as
/pub/unsupported/dos/local/brain12.zip (78 kb) or from
ftp.tu.clausthal.de [139.174.2.10] as /pub/msdos/misc/brain12.zip
(78 kb) If you do not have access to anonymous ftp please contact
me and I will try to email the program to you. 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)
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: 509-627-6CNS; Sysop: Wesley R. Elsberry; P.O.
Box 1187, Richland, WA 99352; welsberr@sandbox.kenn.wa.us. 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.
------------------------------------------------------------------------
o A: Commercial software packages for NN
o ======================================
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$999 per single user
license. Please email us (accel@solomon.technet.sg) for order form.
More information about NeuroForecaster(TM)/Genetical may be found in
ftp.nus.sg incoming/accel.
NeuroForecaster is a user-friendly neural network program specifically
designed for building sophisticated and powerful forecasting and
decision-support systems (Time-Series Forecasting, Cross-Sectional
Classification, Indicator Analysis)
Features:
* GENETICA Net Builder Option for automatic network optimization
* 12 Neuro-Fuzzy Network Models
* Multitasking & Background Training Mode
* Unlimited Network Capacity
* Rescaled Range Analysis & Hurst Exponent to Unveil Hidden Market
Cycles & Check for Predictability
* Correlation Analysis to Compute Correlation Factors to Analyze the
Significance of Indicators
* Weight Histogram to Monitor the Progress of Learning
* Accumulated Error Analysis to Analyze the Strength of Input Indicators
Its user-friendly interface allows the users to build applications quickly,
easily and interactively, analyze the data visually and see the results
immediately.
The following example applications are included in the package:
* Credit Rating - for generating the credit rating of bank loan
applications.
* Stock market 6 monthly returns forecast
* Stock selection based on company ratios
* US$ 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)
------------------------------------------------------------------------
o A: Neural Network hardware?
o ===========================
[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.
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 has already been made 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.
------------------------------------------------------------------------
o A: Databases for experimentation with NNs?
o ==========================================
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 Gamze Erten <ictech@mcimail.com>
o Steve Ward <71561.2370@CompuServe.COM>
o Mohammad Bahrami <bahrami@cse.unsw.edu.au>
o Allen Bonde <ab04@harvey.gte.com>
o Accel Infotech Spore Pte Ltd <accel@solomon.technet.sg>
o Alexander Linden <al@jargon.gmd.de>
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 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 Donald Tveter <drt@mcs.com>
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 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 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 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 Richard Cornelius <richc@rsf.atd.ucar.edu>
o Rob Cunningham <rkc@xn.ll.mit.edu>
o Robert.Kocjancic@IJS.si
o Osamu Saito <saito@nttica.ntt.jp>
o Sheryl Cormicle <sherylc@umich.edu>
o Ted Stockwell <ted@aps1.spa.umn.edu>
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 (email: prechelt@ira.uka.de) | 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.