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From: saswss@unx.sas.com (Warren Sarle)
Subject: comp.ai.neural-nets FAQ, Part 5 of 7: Free software
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Archive-name: ai-faq/neural-nets/part5
Last-modified: 1998-04-10
URL: ftp://ftp.sas.com/pub/neural/FAQ5.html
Maintainer: saswss@unx.sas.com (Warren S. Sarle)

The copyright for the description of each product is held by the producer or
distributor of the product or whoever it was who supplied the description
for the FAQ, who by submitting it for the FAQ gives permission for the
description to be reproduced as part of the FAQ in any of the ways specified
in part 1 of the FAQ. 

This is part 5 (of 7) of a monthly posting to the Usenet newsgroup
comp.ai.neural-nets. See the part 1 of this posting for full information
what it is all about.

========== Questions ========== 
********************************

Part 1: Introduction
Part 2: Learning
Part 3: Generalization
Part 4: Books, data, etc.
Part 5: Free software

   Freeware and shareware packages for NN simulation?

Part 6: Commercial software
Part 7: Hardware and miscellaneous

------------------------------------------------------------------------

Subject: Freeware and shareware packages for NN
===============================================
simulation?
===========

Since the FAQ maintainer works for a software company, he does not recommend
or evaluate software in the FAQ. The descriptions below are provided by the
developers or distributors of the software. 

Note for future submissions: Please restrict software descriptions to a
maximum of 60 lines of 72 characters, in either plain-text format or,
preferably, HTML format. If you include the standard header (name, company,
address, etc.), you need not count the header in the 60 line maximum. Please
confine your HTML to features that are supported by most browsers,
especially NCSA Mosaic 2.0; avoid tables, for example--use <pre> instead.
Try to make the descriptions objective, and avoid making implicit or
explicit assertions about competing products, such as "Our product is the
*only* one that does so-and-so" or "Our innovative product trains bigger
nets faster." The FAQ maintainer reserves the right to remove excessive
marketing hype and to edit submissions to conform to size requirements; if
he is in a good mood, he may also correct spelling and punctuation. 

The following simulators are described below: 

1. Rochester Connectionist Simulator 
2. UCLA-SFINX 
3. NeurDS 
4. PlaNet (formerly known as SunNet) 
5. GENESIS 
6. Mactivation 
7. Cascade Correlation Simulator 
8. Quickprop 
9. DartNet 
10. SNNS 
11. Aspirin/MIGRAINES 
12. Adaptive Logic Network Educational Kit 
13. PDP++ 
14. Uts (Xerion, the sequel) 
15. Neocognitron simulator 
16. Multi-Module Neural Computing Environment (MUME) 
17. LVQ_PAK, SOM_PAK 
18. Nevada Backpropagation (NevProp) 
19. Fuzzy ARTmap 
20. PYGMALION 
21. Basis-of-AI-NN Software 
22. Matrix Backpropagation 
23. BIOSIM 
24. The Brain 
25. FuNeGen 
26. NeuDL -- Neural-Network Description Language 
27. NeoC Explorer 
28. AINET 
29. DemoGNG 
30. PMNEURO 1.0a 
31. nn/xnn 
32. NNDT 
33. Trajan 2.1 Shareware 
34. Neural Networks at your Fingertips 
35. NNFit 
36. Nenet v1.0 
37. Machine Consciousness Toolbox 
38. NICO Toolkit (speech recognition) 
39. SOM Toolbox for Matlab 5 

See also http://www.emsl.pnl.gov:2080/docs/cie/neural/systems/shareware.html

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 ftp.cs.rochester.edu in directory
   pub/packages/simulator as the files README (8 KB), and rcs_v4.2.tar.Z
   (2.9 MB) 

2. UCLA-SFINX
+++++++++++++

      ftp retina.cs.ucla.edu [131.179.16.6];
      Login name: sfinxftp;  Password: joshua;
      directory: pub;
      files : README; sfinx_v2.0.tar.Z;
      Email info request : sfinx@retina.cs.ucla.edu 

3. NeurDS
+++++++++

   simulator for DEC systems supporting VT100 terminal. available for
   anonymous ftp from gatekeeper.dec.com [16.1.0.2] in directory: pub/DEC as
   the file NeurDS031.tar.Z (111 Kb) 

4. PlaNet5.7 (formerly known as SunNet)
+++++++++++++++++++++++++++++++++++++++

   A popular connectionist simulator with versions to run under X Windows,
   and non-graphics terminals created by Yoshiro Miyata (Chukyo Univ.,
   Japan). 60-page User's Guide in Postscript. Send any questions to
   miyata@sccs.chukyo-u.ac.jp Available for anonymous ftp from
   ftp.ira.uka.de as /pub/neuron/PlaNet5.7.tar.Z (800 kb) or from
   boulder.colorado.edu [128.138.240.1] as 
   /pub/generic-sources/PlaNet5.7.tar.Z 

5. GENESIS
++++++++++

   GENESIS 2.0 (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. Runs on most Unix
   platforms. Graphical front end XODUS. Parallel version for networks of
   workstations, symmetric multiprocessors, and MPPs also available.
   Available by ftp from ftp://genesis.bbb.caltech.edu/pub/genesis. Further
   information via WWW at http://www.bbb.caltech.edu/GENESIS/. 

6. Mactivation
++++++++++++++

   A neural network simulator for the Apple Macintosh. Available for ftp
   from ftp.cs.colorado.edu [128.138.243.151] as 
   /pub/cs/misc/Mactivation-3.3.sea.hqx 

7. Cascade Correlation Simulator
++++++++++++++++++++++++++++++++

   A simulator for Scott Fahlman's Cascade Correlation algorithm. Available
   for ftp from ftp.cs.cmu.edu in directory
   /afs/cs/project/connect/code/supported as the file cascor-v1.2.shar (223
   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)
   (There is also an obsolete simulator called quickprop1.c (21 KB) in the
   same directory, but it has been superseeded by NevProp. 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 ftp.dartmouth.edu as 
   /pub/mac/dartnet.sit.hqx (124 KB). 

10. SNNS 4.1
++++++++++++

   "Stuttgarter Neural Network Simulator" from the University of Tuebingen,
   Germany (formerly from the University of Stuttgart): a 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), RBF_DDA, simulated
   annealing, Monte Carlo, Pruned Cascade-Correlation, Optimal Brain Damage,
   Optimal Brain Surgeon, Skeletonization, and is user-extendable
   (user-defined activation functions, output functions, site functions,
   learning procedures). C code generator snns2c. 

   Works on SunOS, Solaris, IRIX, Ultrix, OSF, AIX, HP/UX, NextStep, Linux,
   and Windows 95/NT. Distributed kernel can spread one learning run over a
   workstation cluster. 

   SNNS web page: http://www-ra.informatik.uni-tuebingen.de/SNNS
   Ftp server: ftp://ftp.informatik.uni-tuebingen.de/pub/SNNS
    o SNNSv4.1.Readme 
    o SNNSv4.1.tar.gz (1.4 MB, Source code) 
    o SNNSv4.1.Manual.ps.gz (1 MB, Documentation) 
   Mailing list: 
   http://www-ra.informatik.uni-tuebingen.de/SNNS/about-ml.html 

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/unsupported/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 Educational Kit (for Windows) 
+++++++++++++++++++++++++++++++++++++++++++++++++++++++++

   The Atree 3.0 Educational Kit (EK) serves to develop simple applications
   using adaptive Logic Networks (ALNs). In an ALN, logic functions AND and
   OR make up all hidden layers but the first, which uses simple
   perceptrons. Though this net can't compute real-valued outputs, since
   its outputs are strictly boolean, it can easily and naturally represent
   real valued functions by giving a 0 above the function's graph and a 1
   otherwise. This approach is extremely useful, since it allows the user to
   impose constraints on the functions to be learned (monotonicity, bounds
   on slopes, convexity,...). Very rapid computation of functions is done by
   an ALN decision tree at whose leaves are small expressions of minimum
   and maximum operations acting on linear functions. 

   Two simple languages describe ALNs and the steps of training an ALN.
   Execution software for ALN decision trees resulting from training is
   provided in C source form for experimenters. EK and a "120-page" User's
   Guide are obtained by anonymous ftp from ftp.cs.ualberta.ca in directory
   /pub/atree/atree3/. Get the file atree3ek.exe (~900K) . 

   The above User's Guide with an introduction to basic ALN theory is
   available on WWW at http://www.cs.ualberta.ca/~arms/guide/ch0.htm . This
   Educational Kit software is the same as the commercial Atree 3.0 program
   except that it allows only two input variables and is licensed for
   educational uses only. A built-in 2D and 3D plotting capability is useful
   to help the user understand how ALNs work. 

13. PDP++
+++++++++

   The PDP++ software is a new neural-network simulation system written in
   C++. It represents the next generation of the PDP software released with
   the McClelland and Rumelhart "Explorations in Parallel Distributed
   Processing Handbook", MIT Press, 1987. It is easy enough for novice
   users, but very powerful and flexible for research use.
   The current version is 1.0, our first non-beta release. It has been
   extensively tested and should be completely usable. Works on Unix with
   X-Windows. 

   Features: Full GUI (InterViews), realtime network viewer, data viewer,
   extendable object-oriented design, CSS scripting language with
   source-level debugger, GUI macro recording. 

   Algorithms: Feedforward and several recurrent BP, Boltzmann machine,
   Hopfield, Mean-field, Interactive activation and competition, continuous
   stochastic networks. 

   The software can be obtained by anonymous ftp from 
   ftp://cnbc.cmu.edu/pub/pdp++/ and from 
   ftp://unix.hensa.ac.uk/mirrors/pdp++/. 

   For more information, see our WWW page at 
   http://www.cnbc.cmu.edu/PDP++/PDP++.html.
   There is a 250 page (printed) manual and an HTML version available
   on-line at the above address. 

14. Uts (Xerion, the sequel)
++++++++++++++++++++++++++++

   Uts is a portable artificial neural network simulator written on top of
   the Tool Control Language (Tcl) and the Tk UI toolkit. As result, the
   user interface is readily modifiable and it is possible to simultaneously
   use the graphical user interface and visualization tools and use scripts
   written in Tcl. Uts itself implements only the connectionist paradigm of
   linked units in Tcl and the basic elements of the graphical user
   interface. To make a ready-to-use package, there exist modules which use
   Uts to do back-propagation (tkbp) and mixed em gaussian optimization
   (tkmxm). Uts is available in ftp.cs.toronto.edu in directory /pub/xerion.

15. 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). 

16. 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 for educational institutions by
   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 

17. 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 
   http://nucleus.hut.fi/nnrc/nnrc-programs.html 

18. 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: See:
      ftp://ftp.scs.unr.edu/pub/cbmr/nevpropdir/index.html
      Version 2 has some new features:
      more flexible file formatting (including access to external data files;
      option to prerandomize data order; randomized stochastic gradient descent;
      option to rescale predictor (input) variables); linear output units as an
      alternative to sigmoidal units for use with continuous-valued dependent
      variables (output targets); cross-entropy (maximum likelihood) criterion
      function as an alternative to square error for use with categorical
      dependent variables (classification/symbolic/nominal targets); and
      interactive interrupt to change settings on-the-fly.
      Version 4 will be released in 1998.
      Limited support is available from Phil Goodman (goodman@unr.edu),
      University of Nevada Center for Biomedical Research.

19. Fuzzy ARTmap
++++++++++++++++

   This is just a small example program. Available for anonymous ftp from
   park.bu.edu [128.176.121.56] ftp://cns-ftp.bu.edu/pub/fuzzy-artmap.tar.Z
   (44 kB). 

20. 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). 

21. Basis-of-AI-NN Software
+++++++++++++++++++++++++++

   Non-GUI DOS and UNIX source code, DOS binaries and examples are available
   in the following different program sets and the backprop package has a
   Windows 3.x binary and a Unix/Tcl/Tk version: 

      [backprop, quickprop, delta-bar-delta, recurrent networks],
      [simple clustering, k-nearest neighbor, LVQ1, DSM],
      [Hopfield, Boltzman, interactive activation network],
      [interactive activation network],
      [feedforward counterpropagation],
      [ART I],
      [a simple BAM] and
      [the linear pattern classifier]
      

   For details see: Basis of AI NN software at
   http://www.mcs.com/~drt/svbp.html . 

   An improved professional version of backprop is also available, $30 for
   regular people, $200 for businesses and governmental agencies. See: Basis
   of AI Professional Backprop at http://www.mcs.com/~drt/probp.html . 

   Questions to: Don Tveter, drt@mcs.com 

22. Matrix Backpropagation
++++++++++++++++++++++++++

   MBP (Matrix Back Propagation) is a very efficient implementation of the
   back-propagation algorithm for current-generation workstations. The
   algorithm includes a per-epoch adaptive technique for gradient descent.
   All the computations are done through matrix multiplications and make use
   of highly optimized C code. The goal is to reach almost peak-performances
   on RISCs with superscalar capabilities and fast caches. On some machines
   (and with large networks) a 30-40x speed-up can be measured with respect
   to conventional implementations. The software is available by anonymous
   ftp from risc6000.dibe.unige.it [130.251.89.154] as /pub/MBPv1.1.tar.Z
   (Unix version), /pub/MBPv11.zip.Z (MS-DOS version), /pub/mpbv11.ps
   (Documentation). For more information, contact Davide Anguita
   (anguita@dibe.unige.it). 

23. BIOSIM
++++++++++

   BIOSIM is a biologically oriented neural network simulator. Public
   domain, runs on Unix (less powerful PC-version is available, too), easy
   to install, bilingual (german and english), has a GUI (Graphical User
   Interface), designed for research and teaching, provides online help
   facilities, offers controlling interfaces, batch version is available, a
   DEMO is provided. 

   REQUIREMENTS (Unix version): X11 Rel. 3 and above, Motif Rel 1.0 and
   above, 12 MB of physical memory, recommended are 24 MB and more, 20 MB
   disc space. REQUIREMENTS (PC version): PC-compatible with MS Windows 3.0
   and above, 4 MB of physical memory, recommended are 8 MB and more, 1 MB
   disc space. 

   Four neuron models are implemented in BIOSIM: a simple model only
   switching ion channels on and off, the original Hodgkin-Huxley model, the
   SWIM model (a modified HH model) and the Golowasch-Buchholz model.
   Dendrites consist of a chain of segments without bifurcation. A neural
   network can be created by using the interactive network editor which is
   part of BIOSIM. Parameters can be changed via context sensitive menus and
   the results of the simulation can be visualized in observation windows
   for neurons and synapses. Stochastic processes such as noise can be
   included. In addition, biologically orientied learning and forgetting
   processes are modeled, e.g. sensitization, habituation, conditioning,
   hebbian learning and competitive learning. Three synaptic types are
   predefined (an excitatatory synapse type, an inhibitory synapse type and
   an electrical synapse). Additional synaptic types can be created
   interactively as desired. 

   Available for ftp from ftp.uni-kl.de in directory /pub/bio/neurobio: Get 
   /pub/bio/neurobio/biosim.readme (2 kb) and /pub/bio/neurobio/biosim.tar.Z
   (2.6 MB) for the Unix version or /pub/bio/neurobio/biosimpc.readme (2 kb)
   and /pub/bio/neurobio/biosimpc.zip (150 kb) for the PC version. 

   Contact: 
   Stefan Bergdoll 
   Department of Software Engineering (ZXA/US) 
   BASF Inc. 
   D-67056 Ludwigshafen; Germany 
   bergdoll@zxa.basf-ag.de phone 0621-60-21372 fax 0621-60-43735 

24. The Brain
+++++++++++++

   The Brain is an advanced neural network simulator for PCs that is simple
   enough to be used by non-technical people, yet sophisticated enough for
   serious research work. It is based upon the backpropagation learning
   algorithm. Three sample networks are included. The documentation included
   provides you with an introduction and overview of the concepts and
   applications of neural networks as well as outlining the features and
   capabilities of The Brain. 

   The Brain requires 512K memory and MS-DOS or PC-DOS version 3.20 or later
   (versions for other OS's and machines are available). A 386 (with maths
   coprocessor) or higher is recommended for serious use of The Brain.
   Shareware payment required. 

   Demo version is restricted to number of units the network can handle due
   to memory contraints on PC's. Registered version allows use of extra
   memory. 

   External documentation included: 39Kb, 20 Pages. 
   Source included: No (Source comes with registration). 
   Available via anonymous ftp from ftp.tu-clausthal.de as 
   /pub/msdos/science/brain12.zip (78 kb) and from ftp.technion.ac.il as 
   /pub/contrib/dos/brain12.zip (78 kb) 

   Contact: 
   David Perkovic 
   DP Computing 
   PO Box 712 
   Noarlunga Center SA 5168 
   Australia 
   Email: dip@mod.dsto.gov.au (preferred) or dpc@mep.com or
   perkovic@cleese.apana.org.au 

25. 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 

26. 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). 

27. 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) 

28. AINET
+++++++++

   AINET is a probabilistic neural network application which runs on Windows
   95/NT. It was designed specifically to facilitate the modeling task in
   all neural network problems. It is lightning fast and can be used in
   conjunction with many different programming languages. It does not
   require iterative learning, has no limits in variables (input and output
   neurons), no limits in sample size. It is not sensitive toward noise in
   the data. The database can be changed dynamically. It provides a way to
   estimate the rate of error in your prediction. It has a graphical
   spreadsheet-like user interface. The AINET manual (more than 100 pages)
   is divided into: "User's Guide", "Basics About Modeling with the AINET",
   "Examples", "The AINET DLL library" and "Appendix" where the theoretical
   background is revealed. You can get a full working copy from: 
   http://www.ainet-sp.si/ 

29. DemoGNG
+++++++++++

   This simulator is written in Java and should therefore run without
   compilation on all platforms where a Java interpreter (or a browser with
   Java support) is available. It implements the following algorithms and
   neural network models: 
    o Hard Competitive Learning (standard algorithm) 
    o Neural Gas (Martinetz and Schulten 1991) 
    o Competitive Hebbian Learning (Martinetz and Schulten 1991, Martinetz
      1993) 
    o Neural Gas with Competitive Hebbian Learning (Martinetz and Schulten
      1991) 
    o Growing Neural Gas (Fritzke 1995) 
   DemoGNG is distributed under the GNU General Public License. It allows to
   experiment with the different methods using various probability
   distributions. All model parameters can be set interactively on the
   graphical user interface. A teach modus is provided to observe the models
   in "slow-motion" if so desired. It is currently not possible to
   experiment with user-provided data, so the simulator is useful basically
   for demonstration and teaching purposes and as a sample implementation of
   the above algorithms. 

   DemoGNG can be accessed most easily at 
   http://www.neuroinformatik.ruhr-uni-bochum.de/ in the file 
   /ini/VDM/research/gsn/DemoGNG/GNG.html where it is embedded as Java
   applet into a Web page and is downloaded for immediate execution when you
   visit this page. An accompanying paper entitled "Some competitive
   learning methods" describes the implemented models in detail and is
   available in html at the same server in the directory 
   ini/VDM/research/gsn/JavaPaper/. 

   It is also possible to download the complete source code and a Postscript
   version of the paper via anonymous ftp from
   ftp.neuroinformatik.ruhr-uni-bochum [134.147.176.16] in directory
   /pub/software/NN/DemoGNG/. The software is in the file 
   DemoGNG-1.00.tar.gz (193 KB) and the paper in the file sclm.ps.gz (89
   KB). There is also a README file (9 KB). Please send any comments and
   questions to demogng@neuroinformatik.ruhr-uni-bochum.de which will reach
   Hartmut Loos who has written DemoGNG as well as Bernd Fritzke, the author
   of the accompanying paper. 

30. PMNEURO 1.0a
++++++++++++++++

   PMNEURO 1.0a is available at:

   ftp://ftp.uni-stuttgart.de/pub/systems/os2/misc/pmneuro.zip

   PMNEURO 1.0a creates neuronal networks (backpropagation); propagation
   results can be used as new training input for creating new networks and
   following propagation trials.

31. nn/xnn
++++++++++

      Name: nn/xnn
   Company: Neureka ANS
   Address: Klaus Hansens vei 31B
            5037 Solheimsviken
            NORWAY
     Phone: +47 55 20 15 48
     Email: neureka@bgif.no
       URL: http://www.bgif.no/neureka/ 
   Operating systems: 
        nn: UNIX or MS-DOS, 
       xnn: UNIX/X-windows, UNIX flavours: OSF1, Solaris, AIX, IRIX, Linux (1.2.13)
   System requirements: Min. 20 Mb HD + 4 Mb RAM available. If only the
                        nn/netpack part is used (i.e. not the GUI), much
                        less is needed.
   Approx. price: Free for 30 days after installation, fully functional
                  After 30 days: USD 250,-
                  35% educational discount.

   A comprehensive shareware system for developing and simulating artificial
   neural networks. You can download the software from the URL given above. 

   nn is a high-level neural network specification language. The current
   version is best suited for feed-forward nets, but recurrent models can
   and have been implemented as well. The nn compiler can generate C code or
   executable programs, with a powerful command line interface, but
   everything may also be controlled via the graphical interface (xnn). It
   is possible for the user to write C routines that can be called from
   inside the nn specification, and to use the nn specification as a
   function that is called from a C program. These features makes nn well
   suited for application development. Please note that no programming is
   necessary in order to use the network models that come with the system
   (netpack). 

   xnn is a graphical front end to networks generated by the nn compiler,
   and to the compiler itself. The xnn graphical interface is intuitive and
   easy to use for beginners, yet powerful, with many possibilities for
   visualizing network data. Data may be visualized during training, testing
   or 'off-line'. 

   netpack: A number of networks have already been implemented in nn and can
   be used directly: MAdaline, ART1, Backpropagation, Counterpropagation,
   Elman, GRNN, Hopfield, Jordan, LVQ, Perceptron, RBFNN, SOFM (Kohonen).
   Several others are currently being developed. 

   The pattern files used by the networks, have a simple and flexible
   format, and can easily be generated from other kinds of data. The data
   file generated by the network, can be saved in ASCII or binary format.
   Functions for converting and pre-processing data are available. 

32. NNDT
++++++++


                             NNDT

                 Neural Network Development Tool
                     Evaluation version 1.4
                          Bjvrn Saxen
                             1995

   http://www.abo.fi/~bjsaxen/nndt.html ftp://ftp.abo.fi/pub/vt/bjs/ 

   The NNDT software is as a tool for neural network training. The user
   interface is developed with MS Visual Basic 3.0 professional edition. DLL
   routines (written in C) are used for most of the mathematics. The program
   can be run on a personal computer with MS Windows, version 3.1. 

   Evaluation version
   ------------------

   This evaluation version of NNDT may be used free of charge for personal
   and educational use. The software certainly contains limitations and
   bugs, but is still a working version which has been developed for over
   one year. Comments, bug reports and suggestions for improvements can be
   sent to: 

           bjorn.saxen@abo.fi

   or 

           Bjorn Saxen
           Heat Engineering Laboratory
           Abo Akademi University
           Biskopsgatan 8
           SF-20500 Abo
           Finland

   Remember, this program comes free but with no guarantee! 

   A user's guide for NNDT is delivered in PostScript format. The document
   is split into three parts and compressed into a file called MANUAL.ZIP.
   Due to many bitmap figures included, the total size of the uncompressed
   files is very large, approx 1.5M. 

   Features and methods
   --------------------

   The network algorithms implemented are of the so called supervised type.
   So far, algorithms for multi-layer perceptron (MLP) networks of
   feed-forward and recurrent types are included. The MLP networks are
   trained with the Levenberg-Marquardt method. 

   The training requires a set of input signals and corresponding output
   signals, stored in a file referred to as pattern file. This is the only
   file the user must provide. Optionally, parameters defining the pattern
   file columns, network size and network configuration may be stored in a
   file referred to as setup file. 

   NNDT includes a routine for graphical presentation of output signals,
   node activations, residuals and weights during run. The interface also
   provides facilities for examination of node activations and weights as
   well as modification of weights. 

   A Windows help file is included, help is achieved at any time during NNDT
   execution by pressing F1. 

   Installation
   ------------

   Unzip NNDTxx.ZIP to a separate disk or to a temporary directory e.g. to
   c:\tmp. The program is then installed by running SETUP.EXE. See
   INSTALL.TXT for more details. 

33. Trajan 2.1 Shareware
++++++++++++++++++++++++

   Trajan 2.1 Shareware is a Windows-based Neural Network simulation
   package. It includes support for the two most popular forms of Neural
   Network: Multilayer Perceptrons with Back Propagation and Kohonen
   networks.

   Trajan 2.1 Shareware concentrates on ease-of-use and feedback. It
   includes Graphs, Bar Charts and Data Sheets presenting a range of
   Statistical feedback in a simple, intuitive form. It also features
   extensive on-line Help.

   The Registered version of the package can support very large networks (up
   to 128 layers with up to 8,192 units each, subject to memory limitations
   in the machine), and allows simple Cut and Paste transfer of data to/from
   other Windows-packages, such as spreadsheet programs. The Unregistered
   version features limited network size and no Clipboard Cut-and-Paste.

   There is also a Professional version of Trajan 2.1, which supports a
   wider range of network models, training algorithms and other features.

   See Trajan Software's Home Page at http://www.trajan-software.demon.co.uk
   for further details, and a free copy of the Shareware version.

   Alternatively, email andrew@trajan-software.demon.co.uk for more details.

34. Neural Networks at your Fingertips
++++++++++++++++++++++++++++++++++++++

   "Neural Networks at your Fingertips" is a package of ready-to-reuse
   neural network simulation source code which was prepared for educational
   purposes by Karsten Kutza. The package consists of eight programs, each
   of which implements a particular network architecture together with an
   embedded example application from a typical application domain.
   Supported network architectures are 
    o Adaline, 
    o Backpropagation, 
    o Hopfield Model, 
    o Bidirectional Associative Memory, 
    o Boltzmann Machine, 
    o Counterpropagation, 
    o Self-Organizing Map, and 
    o Adaptive Resonance Theory. 
   The applications demonstrate use of the networks in various domains such
   as pattern recognition, time-series forecasting, associative memory,
   optimization, vision, and control and include e.g. a sunspot prediction,
   the traveling salesman problem, and a pole balancer.
   The programs are coded in portable, self-contained ANSI C and can be
   obtained from the web pages at 
   http://www.geocities.com/CapeCanaveral/1624. 

35. NNFit
+++++++++

   NNFit (Neural Network data Fitting) is a user-friendly software that
   allows the development of empirical correlations between input and output
   data. Multilayered neural models have been implemented using a
   quasi-newton method as learning algorithm. Early stopping method is
   available and various tables and figures are provided to evaluate fitting
   performances of the neural models. The software is available for most of
   the Unix platforms with X-Windows (IBM-AIX, HP-UX, SUN, SGI, DEC, Linux).
   Informations, manual and executable codes (english and french versions)
   are available at http://www.gch.ulaval.ca/~nnfit
   Contact: Bernard P.A. Grandjean, department of chemical engineering,
   Laval University; Sainte-Foy (Quibec) Canada G1K 7P4;
   grandjean@gch.ulaval.ca 

36. Nenet v1.0
++++++++++++++

   Nenet v1.0 is a 32-bit Windows 95 and Windows NT 4.0 application designed
   to facilitate the use of a Self-Organizing Map (SOM) algorithm. 

   The major motivation for Nenet was to create a user-friendly SOM
   algorithm tool with good visualization capabilities and with a GUI
   allowing efficient control of the SOM parameters. The use scenarios have
   stemmed from the user's point of view and a considerable amount of work
   has been placed on the ease of use and versatile visualization methods. 

   With Nenet, all the basic steps in map control can be performed. In
   addition, Nenet also includes some more exotic and involved features
   especially in the area of visualization. 

   Features in Nenet version 1.0: 
    o Implements the standard Kohonen SOM algorithm 
    o Supports 2 common data preprocessing methods 
    o 5 different visualization methods with rectangular or hexagonal
      topology 
    o Capability to animate both train and test sequences in all
      visualization methods 
    o Labelling 
       o Both neurons and parameter levels can be labelled 
       o Provides also autolabelling 
    o Neuron values can be inspected easily 
    o Arbitrary selection of parameter levels can be visualized with Umatrix
      simultaneously 
    o Multiple views can be opened on the same map data 
    o Maps can be printed 
    o Extensive help system provides fast and accurate online help 
    o SOM_PAK compatible file formats 
    o Easy to install and uninstall 
    o Conforms to the common Windows 95 application style - all
      functionality in one application 

   Nenet web site is at: http://www.hut.fi/~jpronkko/nenet.html The web site
   contains further information on Nenet and also the downloadable Nenet
   files (3 disks totalling about 3 Megs) 

   If you have any questions whatsoever, please contact: Nenet-Team@hut.fi
   or phassine@cc.hut.fi 

37. Machine Consciousness Toolbox
+++++++++++++++++++++++++++++++++

   See listing for Machine Consciousness Toolbox in part 6 of the FAQ. 

38. NICO Toolkit (speech recognition)
+++++++++++++++++++++++++++++++++++++

         Name: NICO Artificial Neural Network Toolkit
       Author: Nikko Strom
      Address: Speech, Music and Hearing, KTH, S-100 44, Stockholm, Sweden
        Email: nikko@speech.kth.se
          URL: http://www.speech.kth.se/NICO/index.html
    Platforms: UNIX, ANSI C; Source code tested on: HPUX, SUN Solaris, Linux
        Price: Free

   The NICO Toolkit is an artificial neural network toolkit designed and
   optimized for automatic speech recognition applications. Networks with
   both recurrent connections and time-delay windows are easily constructed.
   The network topology is very flexible -- any number of layers is allowed
   and layers can be arbitrarily connected. Sparse connectivity between
   layers can be specified. Tools for extracting input-features from the
   speech signal are included as well as tools for computing target values
   from several standard phonetic label-file formats. 

   Algorithms: 
    o Back-propagation through time, 
    o Speech feature extraction (Mel cepstrum coefficients, filter-bank) 

o SOM Toolbox for Matlab 5
o ++++++++++++++++++++++++

SOM Toolbox, a shareware Matlab 5 toolbox for data analysis with
self-organizing maps is available at the URL 
http://www.cis.hut.fi/projects/somtoolbox/. If you are interested in
practical data analysis and/or self-organizing maps and have Matlab 5 in
your computer, be sure to check this out! 

Highlights of the SOM Toolbox include the following: 

 o Tools for all the stages of data analysis: besides the basic SOM training
   and visualization tools, the package includes also tools for data
   preprocessing and model validation and interpretation. 
 o Graphical user interface (GUI): the GUI first guides the user through the
   initialization and training procedures, and then offers a variety of
   different methods to visualize the data on the trained map. 
 o Modular programming style: the Toolbox code utilizes Matlab structures,
   and the functions are constructed in a modular manner, which makes it
   convenient to tailor the code for each user's specific needs. 
 o Advanced graphics: building on the Matlab's strong graphics capabilities,
   attractive figures can be easily produced. 
 o Compatibility with SOM_PAK: import/export functions for SOM_PAK codebook
   and data files are included in the package. 
 o Component weights and names: the input vector components may be given
   different weights according to their relative importance, and the
   components can be given names to make the figures easier to read. 
 o Batch or sequential training: in data analysis applications, the speed of
   training may be considerably improved by using the batch version. 
 o Map dimension: maps may be N-dimensional (but visualization is not
   supported when N > 2 ). 

------------------------------------------------------------------------

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 question 
"Other sources of information"). Modem: 409-737-5222; Sysop: Wesley R.
Elsberry; 4160 Pirates' Beach, Galveston, TX, USA; welsberr@orca.tamu.edu.
There are lots of small simulator packages, the CNS ANNSIM file set. There
is an ftp mirror site for the CNS ANNSIM file set at me.uta.edu
[129.107.2.20] in the /pub/neural directory. Most ANN offerings are in 
/pub/neural/annsim. 

------------------------------------------------------------------------

Next part is part 6 (of 7). Previous part is part 4. 

-- 

Warren S. Sarle       SAS Institute Inc.   The opinions expressed here
saswss@unx.sas.com    SAS Campus Drive     are mine and not necessarily
(919) 677-8000        Cary, NC 27513, USA  those of SAS Institute.