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- From: saswss@unx.sas.com (Warren Sarle)
- Newsgroups: comp.ai.neural-nets,comp.answers,news.answers
- Subject: comp.ai.neural-nets FAQ, Part 5 of 7: Free software
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- Archive-name: ai-faq/neural-nets/part5
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- 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
-
- Source code on the web?
- Freeware and shareware packages for NN simulation?
-
- Part 6: Commercial software
- Part 7: Hardware and miscellaneous
-
- ------------------------------------------------------------------------
-
- Subject: Source code on the web?
- ================================
-
- The following URLs are reputed to have source code for NNs. Use at your own
- risk.
-
- o C/C++
- http://www.generation5.org/xornet.shtml
- http://www.netwood.net/~edwin/Matrix/
- http://www.netwood.net/~edwin/svmt/
- http://www.geocities.com/Athens/Agora/7256/c-plus-p.html
- http://www.cs.cmu.edu/afs/cs.cmu.edu/user/mitchell/ftp/faces.html
- http://www.cog.brown.edu/~rodrigo/neural_nets_library.html
- http://www.agt.net/public/bmarshal/aiparts/aiparts.htm
- http://www.geocities.com/CapeCanaveral/1624/
- http://www.neuroquest.com/ or http://www.grobe.org/LANE
- http://www.neuro-fuzzy.de/
- http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/cascor/
- http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/qprop/
- http://www.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/rcc/
- etc.
-
- o Java
- http://www.philbrierley.com/code
- http://rfhs8012.fh-regensburg.de/~saj39122/jfroehl/diplom/e-index.html
- http://neuron.eng.wayne.edu/software.html
- http://www.aist.go.jp/NIBH/~b0616/Lab/Links.html
- http://www.aist.go.jp/NIBH/~b0616/Lab/BSOM1/
- http://www.neuroinformatik.ruhr-uni-bochum.de/ini/PEOPLE/loos
- http://www.neuroinformatik.ruhr-uni-bochum.de/ini/VDM/research/gsn/DemoGNG/GNG.html
- http://www.isbiel.ch/I/Projects/janet/index.html
- http://www.born-again.demon.nl/software.html
- http://www.patol.com/java/NN/index.html
- http://www-isis.ecs.soton.ac.uk/computing/neural/laboratory/laboratory.html
- http://www.neuro-fuzzy.de/
- http://sourceforge.net/projects/joone
- http://joone.sourceforge.net/
- http://openai.sourceforge.net/
- http://www.geocities.com/aydingurel/neural/
- http://www-eco.enst-bretagne.fr/~phan/emergence/complexe/neuron/mlp.html
-
- o FORTRAN
- http://www.philbrierley.com/code
- http://www.cranfield.ac.uk/public/me/fo941992/mlpcode.htm
-
- o Pascal
- http://www.ibrtses.com/delphi/neuralnets.html
-
- 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"). There are lots of small simulator packages.
- Some of the CNS materials can also be found at
- http://www.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/neural/cns/0.html
-
- ------------------------------------------------------------------------
-
- 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 product 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 primitive 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." 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 your spelling and
- punctuation.
-
- The following simulators are described below:
-
- 1. JavaNNS
- 2. SNNS
- 3. PDP++
- 4. Rochester Connectionist Simulator
- 5. UCLA-SFINX
- 6. NeurDS
- 7. PlaNet (formerly known as SunNet)
- 8. GENESIS
- 9. Mactivation
- 10. Cascade Correlation Simulator
- 11. Quickprop
- 12. DartNet
- 13. Aspirin/MIGRAINES
- 14. ALN Workbench
- 15. Uts (Xerion, the sequel)
- 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. FuNeGen
- 25. NeuDL -- Neural-Network Description Language
- 26. NeoC Explorer
- 27. AINET
- 28. DemoGNG
- 29. Trajan 2.1 Shareware
- 30. Neural Networks at your Fingertips
- 31. NNFit
- 32. Nenet v1.0
- 33. Machine Consciousness Toolbox
- 34. NICO Toolkit (speech recognition)
- 35. SOM Toolbox for Matlab 5
- 36. FastICA package for MATLAB
- 37. NEXUS: Large-scale biological simulations
- 38. Netlab: Neural network software for Matlab
- 39. NuTank
- 40. Lens
- 41. Joone: Java Object Oriented Neural Engine
- 42. NV: Neural Viewer
- 43. EasyNN
- 44. Multilayer Perceptron - A Java Implementation
-
- See also
- http://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/shareware.html
-
- 1. JavaNNS: Java Neural Network Simulator
- +++++++++++++++++++++++++++++++++++++++++
-
- http://www-ra.informatik.uni-tuebingen.de/forschung/JavaNNS/welcome_e.html
- JavaNNS is the successor to SNNS. JavaNNS is based on the SNNS computing
- kernel, but has a newly developed graphical user interface written in
- Java set on top of it. Hence compatibility with SNNS is achieved while
- platform-independence is increased.
-
- In addition to SNNS features, JavaNNS offers the capability of linking
- HTML browsers to it. This provides for accessing the user manual
- (available in HTML) or, optionally, a reference coursebook on neural
- networks directly from within the program.
-
- JavaNNS is available for Windows NT / Windows 2000, Solaris and RedHat
- Linux. Additional ports are planed. JavaNNS is freely available and can
- be downloaded from the URL shown above.
-
- Contact: Igor Fischer, Phone: +49 7071 29-77176,
- fischer@informatik.uni-tuebingen.de
-
- 2. SNNS 4.2
- +++++++++++
-
- SNNS (Stuttgart Neural Network Simulator) is a software simulator for
- neural networks on Unix workstations developed at the Institute for
- Parallel and Distributed High Performance Systems (IPVR) at the
- University of Stuttgart. The goal of the SNNS project is to create an
- efficient and flexible simulation environment for research on and
- application of neural nets.
-
- The SNNS simulator consists of two main components:
-
- 1. simulator kernel written in C
- 2. graphical user interface under X11R4 or X11R5
-
- The simulator kernel operates on the internal network data structures of
- the neural nets and performs all operations of learning and recall. It
- can also be used without the other parts as a C program embedded in
- custom applications. It supports arbitrary network topologies and, like
- RCS, supports the concept of sites. SNNS can be extended by the user with
- user defined activation functions, output functions, site functions and
- learning procedures, which are written as simple C programs and linked to
- the simulator kernel. C code can be generated from a trained network.
-
- Currently the following network architectures and learning procedures are
- included:
-
- o Backpropagation (BP) for feedforward networks
- o vanilla (online) BP
- o BP with momentum term and flat spot elimination
- o batch BP
- o chunkwise BP
- o Counterpropagation
- o Quickprop
- o Backpercolation 1
- o RProp
- o Generalized radial basis functions (RBF)
- o ART1
- o ART2
- o ARTMAP
- o Cascade Correlation
- o Dynamic LVQ
- o Backpropagation through time (for recurrent networks)
- o Quickprop through time (for recurrent networks)
- o Self-organizing maps (Kohonen maps)
- o TDNN (time-delay networks) with Backpropagation
- o Jordan networks
- o Elman networks and extended hierarchical Elman networks
- o Associative Memory
- o TACOMA
-
- The graphical user interface XGUI (X Graphical User Interface), built on
- top of the kernel, gives a 2D and a 3D graphical representation of the
- neural networks and controls the kernel during the simulation run. In
- addition, the 2D user interface has an integrated network editor which
- can be used to directly create, manipulate and visualize neural nets in
- various ways.
-
- SNNSv4.1 has been tested on SUN SparcSt ELC,IPC (SunOS 4.1.2, 4.1.3), SUN
- SparcSt 2 (SunOS 4.1.2), SUN SparcSt 5, 10, 20 (SunOS 4.1.3, 5.2),
- DECstation 3100, 5000 (Ultrix V4.2), DEC Alpha AXP 3000 (OSF1 V2.1),
- IBM-PC 80486, Pentium (Linux), IBM RS 6000/320, 320H, 530H (AIX V3.1, AIX
- V3.2), HP 9000/720, 730 (HP-UX 8.07), and SGI Indigo 2 (IRIX 4.0.5, 5.3).
-
- The 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
-
- 3. PDP++
- ++++++++
-
- URL: http://www.cnbc.cmu.edu/PDP++/PDP++.html
-
- The PDP++ software is a 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. PDP++ is featured in a new
- textbook, Computational Explorations in Cognitive Neuroscience:
- Understanding the Mind by Simulating the Brain, by Randall C. O'Reilly
- and Yuko Munakata, MIT Press, 2000.
-
- Supported algorithms include:
-
- o Feedforward and recurrent error backpropagation. Recurrent BP includes
- continuous, real-time models, and Almeida-Pineda.
- o Constraint satisfaction algorithms and associated learning algorithms
- including Boltzmann Machine, Hopfield models, mean-field networks
- (DBM), Interactive Activation and Competition (IAC), and continuous
- stochastic networks.
- o Self-organizing learning including Competitive Learning, Soft
- Competitive Learning, simple Hebbian, and Self-organizing Maps
- ("Kohonen Nets").
- o Mixtures-of-experts using backpropagation experts, EM updating, and a
- SoftMax gating module.
- o Leabra algorithm that combines error-driven and Hebbian learning with
- k-Winners-Take-All inhibitory competition.
-
- The software can be obtained by anonymous ftp from:
- o ftp://grey.colorado.edu/pub/oreilly/pdp++ or
- o ftp://cnbc.cmu.edu/pub/pdp++/ or
- o ftp://unix.hensa.ac.uk/mirrors/pdp++/
-
- 4. Rochester Connectionist Simulator
- ++++++++++++++++++++++++++++++++++++
-
- A 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://ftp.cs.rochester.edu/pub/packages/simulator/simulator_v4.2.tar.Z
- There's also a patch available from
- ftp://ftp.cs.rochester.edu/pub/packages/simulator/simulator_v4.2.patch.1
-
- 5. UCLA-SFINX
- +++++++++++++
-
- The UCLA-SFINX, a "neural" network simulator is now in public domain.
- UCLA-SFINX (Structure and Function In Neural connec- tions) is an
- interactive neural network simulation environment designed to provide the
- investigative tools for studying the behavior of various neural
- structures. It was designed to easily express and simulate the highly
- regular patterns often found in large networks, but it is also general
- enough to model parallel systems of arbitrary interconnectivity. For more
- information, see
- http://decus.acornsw.com/vs0121/AISIG/F90/NETS/UCLA_SIM.TXT
-
- 6. NeurDS
- +++++++++
-
- Neural Design and Simulation System. This is a general purpose tool for
- building, running and analysing Neural Network Models in an efficient
- manner. NeurDS will compile and run virtually any Neural Network Model
- using a consistent user interface that may be either window or "batch"
- oriented. HP-UX 8.07 source code is available from
- http://hpux.u-aizu.ac.jp/hppd/hpux/NeuralNets/NeurDS-3.1/ or
- http://askdonna.ask.uni-karlsruhe.de/hppd/hpux/NeuralNets/NeurDS-3.1/
-
- 7. 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.gz (800 kb)
-
- 8. 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. Further
- information via WWW at http://www.genesis-sim.org/GENESIS/.
-
- 9. Mactivation
- ++++++++++++++
-
- A neural network simulator for the Apple Macintosh. Available for ftp
- from ftp.cs.colorado.edu as /pub/cs/misc/Mactivation-3.3.sea.hqx
-
- 10. 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).
-
- 11. 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.)
-
- 12. 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).
-
- 13. 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
- http://www.elegant-software.com/software/aspirin/
-
- 14. ALN Workbench (a spreadsheet for Windows)
- ++++++++++++++++++++++++++++++++++++++++++++++
-
- ALNBench is a free spreadsheet program for MS-Windows (NT, 95) that
- allows the user to import training and test sets and predict a chosen
- column of data from the others in the training set. It is an easy-to-use
- program for research, education and evaluation of ALN technology. Anyone
- who can use a spreadsheet can quickly understand how to use it. It
- facilitates interactive access to the power of the Dendronic Learning
- Engine (DLE), a product in commercial use.
-
- An ALN consists of linear functions with adaptable weights at the leaves
- of a tree of maximum and minimum operators. The tree grows automatically
- during training: a linear piece splits if its error is too high. The
- function computed by an ALN is piecewise linear and continuous. It can
- learn to approximate any continuous function to arbitrarily high
- accuracy.
-
- Parameters allow the user to input knowledge about a function to promote
- good generalization. In particular, bounds on the weights of the linear
- functions can be directly enforced. Some parameters are chosen
- automatically in standard mode, and are under user control in expert
- mode.
-
- The program can be downloaded from http://www.dendronic.com/main.htm
-
- For further information please contact:
-
- William W. Armstrong PhD, President
- Dendronic Decisions Limited
- 3624 - 108 Street, NW
- Edmonton, Alberta,
- Canada T6J 1B4
- Email: arms@dendronic.com
- URL: http://www.dendronic.com/
- Tel. +1 403 421 0800
- (Note: The area code 403 changes to 780 after Jan. 25, 1999)
-
- 15. 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.
-
- 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. For more information, see
- http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/mume/0.html
-
- 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, version 3, is a relatively easy-to-use, feedforward
- backpropagation multilayer perceptron simulator-that is, statistically
- speaking, a multivariate nonlinear regression program. NevProp3 is
- distributed for free under the terms of the GNU Public License and can be
- downloaded from http://brain.cs.unr.edu/publications/NevProp.zip and
- http://brain.cs.unr.edu/publications/NevPropManual.pdf
-
- The program is distributed as C source code that should compile and run
- on most platforms. In addition, precompiled executables are available for
- Macintosh and DOS platforms. Limited support is available from Phil
- Goodman (goodman@unr.edu), University of Nevada Center for Biomedical
- Research.
-
- MAJOR FEATURES OF NevProp3 OPERATION (* indicates feature new in version
- 3)
- 1. Character-based interface common to the UNIX, DOS, and Macintosh
- platforms.
- 2. Command-line argument format to efficiently initiate NevProp3. For
- Generalized Nonlinear Modeling (GNLM) mode, beginners may opt to use
- an interactive interface.
- 3. Option to pre-standardize the training data (z-score or forced
- range*).
- 4. Option to pre-impute missing elements in training data (case-wise
- deletion, or imputation with mean, median, random selection, or
- k-nearest neighbor).*
- 5. Primary error (criterion) measures include mean square error,
- hyperbolic tangent error, and log likelihood (cross-entropy), as
- penalized an unpenalized values.
- 6. Secondary measures include ROC-curve area (c-index), thresholded
- classification, R-squared and Nagelkerke R-squared. Also reported at
- intervals are the weight configuration, and the sum of square weights.
- 7. Allows simultaneous use of logistic (for dichotomous outputs) and
- linear output activation functions (automatically detected to assign
- activation and error function).*
- 8. 1-of-N (Softmax)* and M-of-N options for binary classification.
- 9. Optimization options: flexible learning rate (fixed global adaptive,
- weight-specific, quickprop), split learn rate (inversely proportional
- to number of incoming connections), stochastic (case-wise updating),
- sigmoidprime offset (to prevent locking at logistic tails).
- 10. Regularization options: fixed weight decay, optional decay on bias
- weights, Bayesian hyperpenalty* (partial and full Automatic Relevance
- Determination-also used to select important predictors), automated
- early stopping (full dataset stopping based on multiple subset
- cross-validations) by error criterion.
- 11. Validation options: upload held-out validation test set; select subset
- of outputs for joint summary statistics;* select automated
- bootstrapped modeling to correct optimistically biased summary
- statistics (with standard deviations) without use of hold-out.
- 12. Saving predictions: for training data and uploaded validation test
- set, save file with identifiers, true targets, predictions, and (if
- bootstrapped models selected) lower and upper 95% confidence limits*
- for each prediction.
- 13. Inference options: determination of the mean predictor effects and
- level effects (for multilevel predictor variables); confidence limits
- within main model or across bootstrapped models.*
- 14. ANN-kNN (k-nearest neighbor) emulation mode options: impute missing
- data elements and save to new data file; classify test data (with or
- without missing elements) using ANN-kNN model trained on data with or
- without missing elements (complete ANN-based expectation
- maximization).*
- 15. AGE (ANN-Gated Ensemble) options: adaptively weight predictions (any
- scale of scores) obtained from multiple (human or computational)
- "experts"; validate on new prediction sets; optional internal
- prior-probability expert.*
-
- 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: http://www.dontveter.com/nnsoft/nnsoft.html
-
- An improved professional version of backprop is also available; see Part
- 6 of the FAQ.
-
- Questions to: Don Tveter, don@dontveter.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 ftp.esng.dibe.unige.it as /neural/MBP/MBPv1.1.tar.Z (Unix
- version), or /neural/MBP/MBPv11.zip (PC version)., 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. FuNeGen 1.0
- +++++++++++++++
-
- FuNeGen is a MLP based software program to generate fuzzy rule based
- classifiers. For more information, see
- http://www-2.cs.cmu.edu/afs/cs.cmu.edu/project/ai-repository/ai/areas/fuzzy/systems/funegen/
-
- 25. 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. For more information, see
- http://www-2.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/neural/systems/neudl/0.html
-
- 26. 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. For more
- information, see http://www.simtel.net/pub/pd/39893.html
-
- 27. 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/
-
- 28. 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.
-
- 29. 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.
-
- 30. 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.
-
- 31. 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
-
- 32. 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.mbnet.fi/~phodju/nenet/Nenet/General.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
-
- 33. Machine Consciousness Toolbox
- +++++++++++++++++++++++++++++++++
-
- See listing for Machine Consciousness Toolbox in part 6 of the FAQ.
-
- 34. 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)
-
- 35. SOM Toolbox for Matlab 5
- ++++++++++++++++++++++++++++
-
- 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 ).
-
- 36. FastICA package for MATLAB
- ++++++++++++++++++++++++++++++
-
- The FastICA algorithm for independent component analysis.
-
- Independent component analysis, or ICA, is neural network or signal
- processing technique that represents a multidimensional random vector as
- a linear combination of nongaussian random variables ('independent
- components') that are as independent as possible. ICA is a nongaussian
- version of factor analysis, and somewhat similar to principal component
- analysis. ICA has many applications in data analysis, source separation,
- and feature extraction.
-
- The FastICA algorithm is a computationally optimized method for
- performing the estimation of ICA. It uses a fixed-point iteration scheme
- that has been found in independent experiments to be 10-100 times faster
- than conventional gradient descent methods for ICA. Another advantage of
- the FastICA algorithm is that it can be used to estimate the independent
- components one-by-one, as in projection pursuit, which is very practical
- in exploratory data analysis.
-
- The FastICA package for MATLAB (versions 5 or 4) is freeware package with
- a graphical user interface that implements the fixed-point algorithm for
- ICA. The package is available on the Web at
- http://www.cis.hut.fi/projects/ica/fastica/.
- Email contact: Aapo Hyvarinen <Aapo.Hyvarinen@hut.fi>
-
- 37. NEXUS: Large-scale biological simulations
- +++++++++++++++++++++++++++++++++++++++++++++
-
- Large-scale biological neural network simulation engine. Includes
- automated network construction tool that allows extremely complex
- networks to be generated according to user-supplied architectural
- specifications.
-
- The network engine is an attempt at creating a biological neural network
- simulator. It consists of a C++ class, called "network". A network object
- houses a set of objects of another C++ class, called "neuron". The neuron
- class is a detailed functional simulation of a neuron (i.e. the actual
- chemical processes that lead to a biological neuron's behavior are not
- modeled explicitly, but the behavior itself is). The simulation of the
- neuron is handled entirely by the neuron class. The network class
- coordinates the functioning of the neurons that make up the neural
- network, as well as providing addressing services that allow the neurons
- to interact. It is also responsible for facilitating the interface of the
- neural network it houses onto any existing software into which the neural
- network is to be integrated.
-
- Since a simulated neural network consisting of a large number of heavily
- interconnected neurons is extremely difficult to generate manually, NEXUS
- was developed. To create a network with NEXUS, one need only describe the
- network in general terms, in terms of groups of sets of specifically
- arranged neurons, and how the groups interface onto each other and onto
- themselves. This information constitutes a network architecture
- descriptor. A network architecture descriptor is read by NEXUS, and NEXUS
- uses the information to generate a network, building all the neurons and
- connecting them together appropriately. This system is analogous to
- nature's brain construction system. For example, human brains, in
- general, are very similar. The basic design is stored in human DNA. Since
- it is certainly not possible to record information about each neuron and
- its connections, DNA must instead contain (in some form) what is
- essentially a set of guidelines, a set of rules about how the brain is to
- be laid out. These guidelines are used to build the brain, just like
- NEXUS uses the guidelines set out in the network architecture descriptor
- to build the simulated neural network.
-
- NEXUS and the network engine have deliberately been engineered to be
- highly efficient and very compact. Even so, large, complex networks
- require tremendous amounts of memory and processing power.
-
- The network engine:
- o flexible and elegant design; highly customizable simulation
- parameters; extremely efficient
- o throughout, nonlinear magnitude decay modeling
- o dendritic tree complexity and network connection density limited only
- by the computer hardware
- o simulation of dendritic logic gate behaviors via a sophisticated
- excitation thresholding and conduction model
- o detailed simulation of backprop, allowing realistic simulation of
- associated memory formation processes
- o simulation of all known postsynaptic memory formation mechanisms (STP,
- STD, LTP, LTD)
- o dynamic presynaptic output pattern modeling, including excitation
- magnitude dependent output pattern selection
- o simulation of all known presynaptic activity-based output modifiers
- (PPF, PTP, depression)
-
- NEXUS:
- o allows networks to be designed concisely and as precisely as is
- necessary
- o makes massively complex large-scale neural network design and
- construction possible
- o allows existing networks to be augmented without disturbing existing
- network structure
- o UNIX and Win32 compatible
-
- URL: http://www.sfu.ca/~loryan/neural.html
- Email: Lawrence O. Ryan <loryan@sfu.ca>
-
- 38. Netlab: Neural network software for Matlab
- ++++++++++++++++++++++++++++++++++++++++++++++
-
- http://www.ncrg.aston.ac.uk/netlab/index.html
-
- The Netlab simulation software is designed to provide the central tools
- necessary for the simulation of theoretically well founded neural network
- algorithms for use in teaching, research and applications development. It
- consists of a library of Matlab functions and scripts based on the
- approach and techniques described in Neural Networks for Pattern
- Recognition by Christopher M. Bishop, (Oxford University Press, 1995).
- The functions come with on-line help, and further explanation is
- available via HTML files.
-
- The Netlab library includes software implementations of a wide range of
- data analysis techniques. Netlab works with Matlab version 5.0 and
- higher. It is not compatible with earlier versions of Matlab.
-
- 39. 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.
-
- NuTank source code is free from
- http://www.xmission.com/~rkeene/NuTankSrc.ZIP
- Contact: Richard Keene; Keene Educational Software
- Email: rkeene@xmission.com or r.keene@center7.com
-
- 40. Lens
- ++++++++
-
- http://www.cs.cmu.edu/~dr/Lens
-
- Lens (the light, efficient network simulator) is a fast, flexible, and
- customizable neural network package written primarily in C. It currently
- handles standard backpropagation networks, simple recurrent (including
- Jordan and Elman) and fully recurrent nets, deterministic Boltzmann
- machines, self-organizing maps, and interactive-activation models.
-
- Lens runs under Windows as well as a variety of Unix platforms. It
- includes a graphical interface and an embedded script language (Tcl). The
- key to the speed of Lens is its use of tight inner-loops that minimize
- memory references when traversing links. Frequently accessed values are
- stored in contiguous memory to achieve good cache performance. It is also
- able to do batch-level parallel training on multiple processors.
-
- Because it is recognized that no simulator will satisfy sophisticated
- users out of the box, Lens was designed to facilitate code modification.
- Users can create and register such things as new network or group types,
- new weight update algorithms, or new shell commands without altering the
- main body of code. Therefore, modifications can be easily transferred to
- new releases.
-
- Lens is available free-of-charge to those conducting research at academic
- or non-profit institutions. Other users should contact Douglas Rohde for
- licensing information at dr+lens@cs.cmu.edu.
-
- 41. Joone: Java Object Oriented Neural Engine
- +++++++++++++++++++++++++++++++++++++++++++++
-
- http://sourceforge.net/projects/joone
-
- Joone is a neural net engine written in Java. It's a modular, scalable,
- multitasking and extensible engine. It can be extended by writing new
- modules to implement new algorithms or new architectures starting from
- simple base components. It's an Open Source project and everybody can
- contribute to its development.
-
- Contact: Paolo Marrone, paolo@marrone.org
-
- 42. NV: Neural Viewer
- +++++++++++++++++++++
-
- http://www.btinternet.com/~cfinnie/
-
- A free software application for modelling and visualizing complex
- recurrent neural networks in 3D.
-
- 43. EasyNN
- ++++++++++
-
- URL: http://www.easynn.com/
-
- EasyNN is a neural network system for Microsoft Windows. It can generate
- multi layer neural networks from text files or grids with minimal user
- intervention. The networks can then be trained, validated and queried.
- Network diagrams, graphs, input/output data and all the network details
- can be displayed and printed. Nodes can be added or deleted while the
- network is learning. The graph, grid, network and detail displays are
- updated dynamically so you can see how the neural networks work. EasyNN
- runs on Windows 95, 98, ME, NT 4.0, 2000 or XP.
-
- 44. Multilayer Perceptron - A Java Implementation
- ++++++++++++++++++++++++++++++++++++++++++++++++++
-
- Download java from: http://www.geocities.com/aydingurel/neural/
-
- What can you exactly do with it? You can:
- o Build nets with any number of layers and units. Layers are connected
- to each other consecutively, each unit in a layer is connected to all
- of the units on the next layer (and vice versa) if there is one,
- o Set units with linear and sigmoid activation functions and set them
- separately for each layer,
- o Set parameters for sigmoid functions and set them separately for each
- layer,
- o Use momentum, set different momentum parameters for each layer,
- o Initialize the net using your own set of weights,
- o Train the net using backpropagation and with any training rate.
-
- Contact: Aydin Gurel, aydin.gurel@lycos.com
-
- ------------------------------------------------------------------------
-
- For some of these simulators there are user mailing lists. Get the packages
- and look into their documentation for further info.
-
- ------------------------------------------------------------------------
-
- 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.
-