1 Simuladores Rna

Enero 2000 ESCOM I P N 1
** Simuladores de** Simuladores de
Redes Neuronales **Redes Neuronales **

Simuladores de RNASimuladores de RNA
The ART GalleryThe ART Gallery
BackBrainBackBrain
Backprop-1.4Backprop-1.4
bpsbps
FuNeGenFuNeGen
Hyperplane AnimatorHyperplane Animator
LVQ PAKLVQ PAK
NETSNETS
NeuralShellNeuralShell
NeuDLNeuDL
NeurfuzzNeurfuzz
NeuroForecaster/GANeuroForecaster/GA
NeuroSolutionsNeuroSolutions
NevPropNevProp

Simuladores de RNASimuladores de RNA
NICONICO
nn/xnnnn/xnn
PDP SoftwarePDP Software
PittnetPittnet
SOM PAKSOM PAK
SPIDER Web NeuralSPIDER Web Neural
Network LibraryNetwork Library
TDNNTDNN
tlearntlearn
WinNNWinNN
Xerion SimulatorXerion Simulator
Neural NetworkNeural Network
ToolboxToolbox

The ART GalleryThe ART Gallery
Descripción: ART Gallery es una serie deDescripción: ART Gallery es una serie de
procedimientos dedicados a ser usadosprocedimientos dedicados a ser usados
con otros codigos para implementar redescon otros codigos para implementar redes
neuronales de tipo ART.neuronales de tipo ART.
Plataforma: Windows , UNIXPlataforma: Windows , UNIX
Desarrolladores: Lars H. LidenDesarrolladores: Lars H. Liden

BackBrainBackBrain
Descripción: BackBrain simula redes de tipoDescripción: BackBrain simula redes de tipo
Backpropagation; permite, crear,entrenarBackpropagation; permite, crear,entrenar
y analizar redes. Tambien crea modelos eny analizar redes. Tambien crea modelos en
3D de redes dinámicas.3D de redes dinámicas.
Plataforma: Power Macintosh with Sistem 7Plataforma: Power Macintosh with Sistem 7
Desarrollador: University of SouthamptonDesarrollador: University of Southampton
UK.UK.

Backprop-1.4Backprop-1.4
Descripción: Programa manipulado porDescripción: Programa manipulado por
*Mouse* permite diseñar redes de forma*Mouse* permite diseñar redes de forma
grafica; el sistema esta limitado a redesgrafica; el sistema esta limitado a redes
con un maximo de 25 neuronas . Fuecon un maximo de 25 neuronas . Fue
desarrollado con el proposito dedesarrollado con el proposito de
aprendizaje de redes Backpropagation.aprendizaje de redes Backpropagation.
Plataforma: DOSPlataforma: DOS
Desarrollador: University of KasselDesarrollador: University of Kassel

bpsbps
Descripción: Sistema para el desarrollo deDescripción: Sistema para el desarrollo de
redes entrenadas por el algoritmo deredes entrenadas por el algoritmo de
retropropagación de error.retropropagación de error.
Plataformas: PC, VAX, MAC.Plataformas: PC, VAX, MAC.
Desarrollador: Eugene Norris, ComputerDesarrollador: Eugene Norris, Computer
Science Deparment; Georgr MasonScience Deparment; Georgr Mason
University, Virginia USA.University, Virginia USA.

FuNeGenFuNeGen
Descripción: Esta basado en los conceptosDescripción: Esta basado en los conceptos
de sistemas neurodifusos, puede generarde sistemas neurodifusos, puede generar
sistemas de clasificación difusa desistemas de clasificación difusa de
información muestreada, no hay limitantesinformación muestreada, no hay limitantes
en cuanto al numero de entradas y salidas;en cuanto al numero de entradas y salidas;
además permite eliminar entradasademás permite eliminar entradas
redundantes de manera automática.redundantes de manera automática.
Desarrollador:Darmstadt University of Tech.Desarrollador:Darmstadt University of Tech.

Hyperplane AnimatorHyperplane Animator
Descripción: Hyperplane Animator es unDescripción: Hyperplane Animator es un
programa que permite fácilmente deprograma que permite fácilmente de
manera gráfica el entrenamiento de redesmanera gráfica el entrenamiento de redes
neuronales de retropropagación.neuronales de retropropagación.
Desarrollador: Paul Hoeper and Lori Pratt;Desarrollador: Paul Hoeper and Lori Pratt;
Rutgers UniversityRutgers University

LVQ PAKLVQ PAK
Descripción: Es un grupo de metodosDescripción: Es un grupo de metodos
aplicables al reconocimiento estadistico deaplicables al reconocimiento estadistico de
patrones, en las cuales las clases sonpatrones, en las cuales las clases son
descritas por un numero relativamentedescritas por un numero relativamente
pequeño de vectores codigo.pequeño de vectores codigo.
Desarrollador: Teuvo Kohonen, HelsinkiDesarrollador: Teuvo Kohonen, Helsinki
University of Technology; FinlandiaUniversity of Technology; Finlandia

NETSNETS
Descripción: Network Execution and TrainingDescripción: Network Execution and Training
Simulator (NETS) Es una herramienta la cualSimulator (NETS) Es una herramienta la cual
proporciona un ambiente para el desarrollo yproporciona un ambiente para el desarrollo y
evaluación de redes neuronales. El sistemaevaluación de redes neuronales. El sistema
permite crear y ejecutar configuracionespermite crear y ejecutar configuraciones
arbitrarias de redes las cuales usan aprendizajearbitrarias de redes las cuales usan aprendizaje
de retropropagación.de retropropagación.
Desarrollador: COSMIC, University of GeorgiaDesarrollador: COSMIC, University of Georgia

Neural NetworksNeural Networks
at your Firgertipsat your Firgertips
Descripción: simulador de las 8 mas popularesDescripción: simulador de las 8 mas populares
arquitecturas de redes neuronales; codigoarquitecturas de redes neuronales; codigo
portable , autocontenido en ANSI C.portable , autocontenido en ANSI C.
Algoritmos: Adaline, Backpropagation, Hopfield,Algoritmos: Adaline, Backpropagation, Hopfield,
Memoria Asociativa Bidireccional, maquina deMemoria Asociativa Bidireccional, maquina de
Bolzmann, counterpropagation, SOM, ART.Bolzmann, counterpropagation, SOM, ART.
Desarrollador:Karsten Kutza, Berlin Alemania.Desarrollador:Karsten Kutza, Berlin Alemania.

NeuralShellNeuralShell
Descripción: Es un Shell el cual llama simuladoresDescripción: Es un Shell el cual llama simuladores
individuales de redes neuronales artificiales.individuales de redes neuronales artificiales.
Algoritmos: Hopfield, Hamming, Backpropagation,Algoritmos: Hopfield, Hamming, Backpropagation,
Mapas de Kohonen, Aprendizaje Competitivo,Mapas de Kohonen, Aprendizaje Competitivo,
Retropropagación Adaptativa.Retropropagación Adaptativa.
Plataforma: UNIX (SUN, Cray).Plataforma: UNIX (SUN, Cray).
Desarrollador: SPANN Laboratory, Ohio StateDesarrollador: SPANN Laboratory, Ohio State
University, columbus, USA.University, columbus, USA.

NeuroSolutionsNeuroSolutions
Descripción: Sistema consistente de un conjunto deDescripción: Sistema consistente de un conjunto de
tutoriales de diferentes tipos de redes entre lastutoriales de diferentes tipos de redes entre las
cuales están, Perceptron, asociador lineal, filtroscuales están, Perceptron, asociador lineal, filtros
adaptativos, redes jordan-elman, Mapas deadaptativos, redes jordan-elman, Mapas de
Kohonen, redes de base radial, etc. El softwareKohonen, redes de base radial, etc. El software
permite construir y entrenar redes neuronalespermite construir y entrenar redes neuronales
además genera código ANSI C/C++.además genera código ANSI C/C++.
Plataforma: Windows 95.Plataforma: Windows 95.
Desarrollador: Neurodimension inc.Desarrollador: Neurodimension inc.

NeuDLNeuDL
Neural Network Description Lenguage es unaNeural Network Description Lenguage es una
nueva herramienta con un lenguaje denueva herramienta con un lenguaje de
programación interprete, dedicado a laprogramación interprete, dedicado a la
construcción, entrenamiento, prueba y corridasconstrucción, entrenamiento, prueba y corridas
de diseños de redes neuronales. Actualmente,de diseños de redes neuronales. Actualmente,
esta limitada a redes tipo backpropagation.esta limitada a redes tipo backpropagation.
Desarrollador:Joy Rogers, University ofDesarrollador:Joy Rogers, University of AlabamaAlabama

NeurfuzzNeurfuzz
Descripción: Neurofuzz 1.0 es un generadorDescripción: Neurofuzz 1.0 es un generador
de código C para sistemas difusos y redesde código C para sistemas difusos y redes
neuronales artificiales tiponeuronales artificiales tipo
Backpropagation.Backpropagation.
Desarrollador: Luca Marchese.Desarrollador: Luca Marchese.

NeuroForecaster/GANeuroForecaster/GA
Descripción: NeuroForecaster/GA Versión 7.0 esDescripción: NeuroForecaster/GA Versión 7.0 es
una red neuronal de 32 bits y algoritmosuna red neuronal de 32 bits y algoritmos
geneticos basados en programas de prediccióngeneticos basados en programas de predicción
orientados a finanzas y negocios.orientados a finanzas y negocios.
Algoritmos: Neurogeneticos.Algoritmos: Neurogeneticos.
Desarrollador: NIBS Inc .Desarrollador: NIBS Inc .

NevPropNevProp
Descripción: NevProp es un programa fácil de usarDescripción: NevProp es un programa fácil de usar
para redes feedforward tipo perceptronpara redes feedforward tipo perceptron
multicapa y Back propagation. Usa una interfazmulticapa y Back propagation. Usa una interfaz
interactiva basada en caracteres.interactiva basada en caracteres.
Algoritmos: Quick Propagation.Algoritmos: Quick Propagation.
Plataforma: DOS, Macintosh, Unix.Plataforma: DOS, Macintosh, Unix.
Desarrollador: University of Nevada at RenoDesarrollador: University of Nevada at Reno

NICO Artificial NeuralNICO Artificial Neural
Network ToolkitNetwork Toolkit
Descripción: Es una herramienta de desarrollo de redesDescripción: Es una herramienta de desarrollo de redes
neuronales, diseñada y optimizadas para elneuronales, diseñada y optimizadas para el
reconocimiento automatico de voz; se pueden construirreconocimiento automatico de voz; se pueden construir
redes con conexiones recurrentes y retardos, la topologiaredes con conexiones recurrentes y retardos, la topologia
de las redes es muy flexible, permite cualquier numero dede las redes es muy flexible, permite cualquier numero de
capas y las cuales pueden ser arbitrariamentecapas y las cuales pueden ser arbitrariamente
conectadas.conectadas.
Plataforma : UNIX,codigo fuente ANSI-C en :HPUX, SUNPlataforma : UNIX,codigo fuente ANSI-C en :HPUX, SUN
Solaris, Linux.Solaris, Linux.
Desarrollador: Nikko Strom, Speech music and Hearing,Desarrollador: Nikko Strom, Speech music and Hearing,
Stockholm Sweden.Stockholm Sweden.

nn/xnnnn/xnn
Descripción : nn/xnn es un sistema para el desarrollo yDescripción : nn/xnn es un sistema para el desarrollo y
simulación de redes neuronales. Nn es un lenguaje desimulación de redes neuronales. Nn es un lenguaje de
alto nivel para la especificación de redes neuronales,alto nivel para la especificación de redes neuronales,
dicho compilador puede generar codigo en C odicho compilador puede generar codigo en C o
programas ejecutables; al usar los modelos incluidos enprogramas ejecutables; al usar los modelos incluidos en
el sistema la programación no es necesaria.el sistema la programación no es necesaria.
Algoritmos: Madaline, Backpropagation, ART1,Algoritmos: Madaline, Backpropagation, ART1,
counterpropagation, Elman,GRNN, Hopfield, Jordan,counterpropagation, Elman,GRNN, Hopfield, Jordan,
LVQ, Perceptron, Redes de base radial, Mapas deLVQ, Perceptron, Redes de base radial, Mapas de
Kohonen.Kohonen.
Desarrollador: Neureka ANS, Solheimsviken, Norway.Desarrollador: Neureka ANS, Solheimsviken, Norway.

PDP SoftwarePDP Software
Descripción: Simulador de procesosDescripción: Simulador de procesos
distribuidos en paralelo.distribuidos en paralelo.
Algoritmos: Redes Feedforward y variasAlgoritmos: Redes Feedforward y varias
redes recurrentes , Maquina de Bolzmann,redes recurrentes , Maquina de Bolzmann,
hopfield, redes estocasticas continuas.hopfield, redes estocasticas continuas.
Plataforma: UNIX, MSDOS.Plataforma: UNIX, MSDOS.
Desarrollador:Desarrollador:

PittnetPittnet
Descripción: El proposito del sistema es permitirDescripción: El proposito del sistema es permitir
sal usuario construir, entrenar y probarsal usuario construir, entrenar y probar
diferentes tipos de redes neuronales.diferentes tipos de redes neuronales.
Algoritmos: Redes Feedforward conAlgoritmos: Redes Feedforward con
backpropagation, ART1, SOM, RBF.backpropagation, ART1, SOM, RBF.
Plataforma: DOS y codigo fuente C++.Plataforma: DOS y codigo fuente C++.
Desarrollador: Brian Carnahan y alice E. Smith,Desarrollador: Brian Carnahan y alice E. Smith,
University of Pittsburgh, USAUniversity of Pittsburgh, USA

SpiderWeb NeuralSpiderWeb Neural
Network LibraryNetwork Library
Descripción: Codigo fuente C++ para implementarDescripción: Codigo fuente C++ para implementar
redes neuronales; esta diseñado para serredes neuronales; esta diseñado para ser
facilmente extendido a aumentar susfacilmente extendido a aumentar sus
capacidades.capacidades.
Algoritmos: Backpropagation.Algoritmos: Backpropagation.
Plataforma: Codigo fuente C++.Plataforma: Codigo fuente C++.
Desarrollador: Robert KlapperDesarrollador: Robert Klapper

Time Delay NeuralTime Delay Neural
Network - TDNNNetwork - TDNN
Descripción: El sistema consiste de una red conDescripción: El sistema consiste de una red con
una topologia fija predefinida para eluna topologia fija predefinida para el
reconocimiento de digitos hablados del 0 al 9reconocimiento de digitos hablados del 0 al 9
partiendo de voz continua, la capa de entradapartiendo de voz continua, la capa de entrada
consiste de un arreglo de 16 x 11 unidades.consiste de un arreglo de 16 x 11 unidades.
Plataforma: DOS.Plataforma: DOS.
Desarrollador: University de Ulm.Desarrollador: University de Ulm.

tlearntlearn
Descrpción: tlearn es un simulador de redesDescrpción: tlearn es un simulador de redes
neuronales la cual implementa la regla deneuronales la cual implementa la regla de
aprendizaje de retropropagación, incluye redesaprendizaje de retropropagación, incluye redes
recurrentes simples; icluye un editor de textos yrecurrentes simples; icluye un editor de textos y
un gran numero de utilerias para el analisis deun gran numero de utilerias para el analisis de
datos.datos.
Plataformas: Mac OS 7.5+, Windows 95, Unix.Plataformas: Mac OS 7.5+, Windows 95, Unix.
Desarrollador: Kim Plunkett y Jeffrey L. ElmanDesarrollador: Kim Plunkett y Jeffrey L. Elman

WinNNWinNN
Descripción: WinNN incorpora una interfaz amigable muyDescripción: WinNN incorpora una interfaz amigable muy
util ademas de un gran potencial computacional.util ademas de un gran potencial computacional.
WinNN es una herramienta que esta dedicada aWinNN es una herramienta que esta dedicada a
usuarios principiantes y mas avanzados de redesusuarios principiantes y mas avanzados de redes
neuronales. Permite implementar redes feeforwardneuronales. Permite implementar redes feeforward
multicapa utilizando el algoritmo de retropropagaciónmulticapa utilizando el algoritmo de retropropagación
para su entrenamiento.para su entrenamiento.
Algoritmo: Backpropagation.Algoritmo: Backpropagation.
Plataforma: MS-WindowsPlataforma: MS-Windows

Xerion SimulatorXerion Simulator
Descripción: Xerion esta conformado por un conjunto deDescripción: Xerion esta conformado por un conjunto de
bibliotecas en C que pueden ser usadas para labibliotecas en C que pueden ser usadas para la
construcción de redes neuronales experimentalesconstrucción de redes neuronales experimentales
complejas, y preconstruir simuladores escritos con estascomplejas, y preconstruir simuladores escritos con estas
bibliotecas.bibliotecas.
Algoritmos: Backpropagation, Backpropagation recurrente,Algoritmos: Backpropagation, Backpropagation recurrente,
Maquina de Bolzmann, SOM, LVQ, FEM, CL.Maquina de Bolzmann, SOM, LVQ, FEM, CL.
Plataforma: Silicon Graphics and SUN.Plataforma: Silicon Graphics and SUN.
Desarrollador: Xerion Project, University of TorontoDesarrollador: Xerion Project, University of Toronto

Neural NetworkNeural Network
Toolbox (Matlab)Toolbox (Matlab)
Descripción: Herramienta para el desarrollo yDescripción: Herramienta para el desarrollo y
entrenamiento de redes neuronales bajo elentrenamiento de redes neuronales bajo el
ambiente de Matlab. Redes de tipo perceptron,ambiente de Matlab. Redes de tipo perceptron,
adaline, backpropagation, redes de base radial,adaline, backpropagation, redes de base radial,
SOM, Elman, Hopfield, LVQ.SOM, Elman, Hopfield, LVQ.
Plataforma: Windows 95, 98.Plataforma: Windows 95, 98.
Desarrollador: Mathworks.Desarrollador: Mathworks.

ReferenciasReferencias
Pacific North NationalPacific North National
Avaliable software: Artificial Neural NetworksAvaliable software: Artificial Neural Networks..
Http://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/shareware.htmlHttp://www.emsl.pnl.gov:2080/proj/neuron/neural/systems/shareware.html
CNET Shareware.comCNET Shareware.com
Busqueda: Neural NetworksBusqueda: Neural Networks

18. A: Commercial software packages18. A: Commercial software packages
for NN simulation?for NN simulation?
============================================================
======================== 1.======================== 1.
nn/xnn +++++++++ Name: nn/xnnnn/xnn +++++++++ Name: nn/xnn
Company: Neureka ANS Address:Company: Neureka ANS Address:
Klaus Hansens vei 31B 5037Klaus Hansens vei 31B 5037
Solheimsviken NORWAY Phone: +47-Solheimsviken NORWAY Phone: +47-
55544163 / +47-55201548 Email:55544163 / +47-55201548 Email:
arnemo@eik.ii.uib.no Basicarnemo@eik.ii.uib.no Basic
capabilities: Neural networkcapabilities: Neural network
development tool. nn is a language fordevelopment tool. nn is a language for
specification of neural networkspecification of neural network
simulators. Produces C-code andsimulators. Produces C-code and
executables for the specified models,executables for the specified models,
therefore ideal for applicationtherefore ideal for application

Gives graphical representations in aGives graphical representations in a
number of formats of any variablesnumber of formats of any variables
during simulation run-time. Comesduring simulation run-time. Comes
with a number of pre-implementedwith a number of pre-implemented
models, including: Backprop (severalmodels, including: Backprop (several
variants), Self Organizing Maps,variants), Self Organizing Maps,
LVQ1, LVQ2, Radial Basis FunctionLVQ1, LVQ2, Radial Basis Function
Networks, Generalized RegressionNetworks, Generalized Regression
Neural Networks, Jordan nets, ElmanNeural Networks, Jordan nets, Elman
nets, Hopfield, etc. Operating system:nets, Hopfield, etc. Operating system:
nn: UNIX or MS-DOS, xnn: UNIX/X-nn: UNIX or MS-DOS, xnn: UNIX/X-
windows System requirements: 10 Mbwindows System requirements: 10 Mb

2. BrainMaker +++++++++++++ Name:2. BrainMaker +++++++++++++ Name:
BrainMaker, BrainMaker Pro Company:BrainMaker, BrainMaker Pro Company:
California Scientific Software Address:California Scientific Software Address:
10024 Newtown rd, Nevada City, CA,10024 Newtown rd, Nevada City, CA,
95959 USA Phone,Fax: 916 478 9040,95959 USA Phone,Fax: 916 478 9040,
916 478 9041 Email: calsci!916 478 9041 Email: calsci!
mittmann@gvgpsa.gvg.tek.com (flakeymittmann@gvgpsa.gvg.tek.com (flakey
connection) Basic capabilities: trainconnection) Basic capabilities: train
backprop neural nets Operatingbackprop neural nets Operating
system: DOS, Windows, Mac Systemsystem: DOS, Windows, Mac System
requirements: Uses XMS or EMS forrequirements: Uses XMS or EMS for
large models(PCs only): Pro versionlarge models(PCs only): Pro version
Approx. price: $195, $795 BrainMakerApprox. price: $195, $795 BrainMaker
Pro 3.0 (DOS/Windows)Pro 3.0 (DOS/Windows)

$795 Gennetic Training add-on $250$795 Gennetic Training add-on $250
ainMaker 3.0 (DOS/Windows/Mac)ainMaker 3.0 (DOS/Windows/Mac)
$195 Network Toolkit add-on $150$195 Network Toolkit add-on $150
BrainMaker 2.5 Student versionBrainMaker 2.5 Student version
(quantity sales only, about $38 each)(quantity sales only, about $38 each)
BrainMaker Pro C30 Accelerator BoardBrainMaker Pro C30 Accelerator Board
w/ 5Mb memory $9750 w/32Mbw/ 5Mb memory $9750 w/32Mb
memory $13,000 Intel iNNTS NNmemory $13,000 Intel iNNTS NN
Development System $11,800Development System $11,800

Intel EMB Multi-Chip Board $9750 IntelIntel EMB Multi-Chip Board $9750 Intel
80170 chip set $940 Introduction To80170 chip set $940 Introduction To
Neural Networks book $30 CaliforniaNeural Networks book $30 California
Scientific Software can be reached at:Scientific Software can be reached at:
Phone: 916 478 9040 Fax: 916 478Phone: 916 478 9040 Fax: 916 478
9041 Tech Support: 916 478 90359041 Tech Support: 916 478 9035
Mail: 10024 newtown rd, Nevada City,Mail: 10024 newtown rd, Nevada City,
CA, 95959, USA 30 day money backCA, 95959, USA 30 day money back
guarantee, and unlimited freeguarantee, and unlimited free
technical support. BrainMaker packagetechnical support. BrainMaker package
includes: The book Introduction toincludes: The book Introduction to
Neural Networks BrainMaker UsersNeural Networks BrainMaker Users

Netmaker makes building and trainingNetmaker makes building and training
Neural Networks easy, by importingNeural Networks easy, by importing
and automatically creatingand automatically creating
BrainMaker's Neural Network files.BrainMaker's Neural Network files.
Netmaker imports Lotus, Excel, dBase,Netmaker imports Lotus, Excel, dBase,
and ASCII files. BrainMaker Full menuand ASCII files. BrainMaker Full menu
and dialog box interface, runsand dialog box interface, runs
Backprop at 750,000 cps on a 33MhzBackprop at 750,000 cps on a 33Mhz
486. ---Features ("P" means is486. ---Features ("P" means is
avaliable in professional version only):avaliable in professional version only):
Pull-down Menus, Dialog Boxes,Pull-down Menus, Dialog Boxes,
Programmable Output Files, Editing inProgrammable Output Files, Editing in

Dynamic Data Exchange (P), BinaryDynamic Data Exchange (P), Binary
Data Mode, Batch Use Mode (P), EMSData Mode, Batch Use Mode (P), EMS
and XMS Memory (P), Save Networkand XMS Memory (P), Save Network
Periodically, Fastest Algorithms, 512Periodically, Fastest Algorithms, 512
Neurons per Layer (P: 32,000), up to 8Neurons per Layer (P: 32,000), up to 8
layers, Specify Parameters by Layerlayers, Specify Parameters by Layer
(P), Recurrence Networks (P), Prune(P), Recurrence Networks (P), Prune
Connections and Neurons (P), AddConnections and Neurons (P), Add
Hidden Neurons In Training, CustomHidden Neurons In Training, Custom
Neuron Functions, Testing WhileNeuron Functions, Testing While
Training, Stop training when...-Training, Stop training when...-
function (P), Heavy Weights (P),function (P), Heavy Weights (P),

Global Network Analysis (P), ContourGlobal Network Analysis (P), Contour
Analysis (P), Data Correlator (P),Analysis (P), Data Correlator (P),
Error Statistics Report, Print or EditError Statistics Report, Print or Edit
Weight Matrices, Competitor (P), RunWeight Matrices, Competitor (P), Run
Time System (P), Chip Support forTime System (P), Chip Support for
Intel, American Neurologics, MicroIntel, American Neurologics, Micro
Devices, Genetic Training Option (P),Devices, Genetic Training Option (P),
NetMaker, NetChecker, Shuffle, DataNetMaker, NetChecker, Shuffle, Data
Import from Lotus, dBASE, Excel,Import from Lotus, dBASE, Excel,
ASCII, binary, Finacial Data (P), DataASCII, binary, Finacial Data (P), Data
Manipulation, Cyclic Analysis (P),Manipulation, Cyclic Analysis (P),
User's Guide quick start booklet,User's Guide quick start booklet,

3. SAS Software/ Neural Net add-on +++3. SAS Software/ Neural Net add-on +++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+ Name: SAS Software Company: SAS+ Name: SAS Software Company: SAS
Institute, Inc. Address: SAS CampusInstitute, Inc. Address: SAS Campus
Drive, Cary, NC 27513, USADrive, Cary, NC 27513, USA
Phone,Fax: (919) 677-8000 Email:Phone,Fax: (919) 677-8000 Email:
saswss@unx.sas.com (Neural netsaswss@unx.sas.com (Neural net
inquiries only) Basic capabilities:inquiries only) Basic capabilities:
Feedforward nets with numerousFeedforward nets with numerous
training methods and loss functions,training methods and loss functions,
plus statistical analogs ofplus statistical analogs of
counterpropagation and variouscounterpropagation and various
unsupervised architectures Operatingunsupervised architectures Operating
system: Lots System requirements:system: Lots System requirements:
Lots Uses XMS or EMS for largeLots Uses XMS or EMS for large
models(PCs only):models(PCs only):

Runs under Windows, OS/2 Approx.Runs under Windows, OS/2 Approx.
price: Free neural net software, butprice: Free neural net software, but
you have to license SAS/Baseyou have to license SAS/Base
software and preferably the SAS/OR,software and preferably the SAS/OR,
SAS/ETS, and/or SAS/STAT products.SAS/ETS, and/or SAS/STAT products.
Comments: Oriented toward dataComments: Oriented toward data
analysis and statistical applicationsanalysis and statistical applications

4. NeuralWorks ++++++++++++++4. NeuralWorks ++++++++++++++
Name: NeuralWorks Professional IIName: NeuralWorks Professional II
Plus (from NeuralWare) Company:Plus (from NeuralWare) Company:
NeuralWare Inc. Adress: Pittsburgh,NeuralWare Inc. Adress: Pittsburgh,
PA 15276-9910 Phone: (412) 787-8222PA 15276-9910 Phone: (412) 787-8222
FAX: (412) 787-8220 Distributor forFAX: (412) 787-8220 Distributor for
Europe: Scientific Computers GmbH.Europe: Scientific Computers GmbH.
Franzstr. 107, 52064 Aachen GermanyFranzstr. 107, 52064 Aachen Germany
Tel. (49) +241-26041 Fax. (49) +241-Tel. (49) +241-26041 Fax. (49) +241-
44983 Email. info@scientific.de Basic44983 Email. info@scientific.de Basic
capabilities: supports over 30 differentcapabilities: supports over 30 different
nets: backprop, art-1,kohonen,nets: backprop, art-1,kohonen,
modular neural network, Generalmodular neural network, General
regression, Fuzzy art-map,regression, Fuzzy art-map,
probabilistic nets, self-organizing map,probabilistic nets, self-organizing map,
lvq, boltmann, bsb, spr, etc...lvq, boltmann, bsb, spr, etc...

ExplainNet, Flashcode (compiles netExplainNet, Flashcode (compiles net
in .c code for runtime), user-defined ioin .c code for runtime), user-defined io
in c possible. ExplainNet (to eliminatein c possible. ExplainNet (to eliminate
extra inputs), pruning,extra inputs), pruning,
savebest,graph.instruments likesavebest,graph.instruments like
correlation, hinton diagrams, rms errorcorrelation, hinton diagrams, rms error
graphs etc.. Operating system :graphs etc.. Operating system :
PC,Sun,IBM RS6000,ApplePC,Sun,IBM RS6000,Apple
Macintosh,SGI,Dec,HP. SystemMacintosh,SGI,Dec,HP. System
requirements: varies. PC:2MBrequirements: varies. PC:2MB
extended memory+6MB Harddiskextended memory+6MB Harddisk
space. Uses windows compatiblespace. Uses windows compatible
memory driver (extended). Usesmemory driver (extended). Uses
extended memory. Approx. price : callextended memory. Approx. price : call
(depends on platform) Comments :(depends on platform) Comments :
award winning documentation, one ofaward winning documentation, one of

5. MATLAB Neural Network Toolbox (for5. MATLAB Neural Network Toolbox (for
use with Matlab 4.x) ++++++++++++++use with Matlab 4.x) ++++++++++++++
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
++++++++++++++ Contact: The++++++++++++++ Contact: The
MathWorks, Inc. Phone: 508-653-1415MathWorks, Inc. Phone: 508-653-1415
24 Prime Park Way FAX: 508-653-24 Prime Park Way FAX: 508-653-
2997 Natick, MA 01760 email:2997 Natick, MA 01760 email:
info@mathworks.com The Neuralinfo@mathworks.com The Neural
Network Toolbox is a powerfulNetwork Toolbox is a powerful
collection of MATLAB functions for thecollection of MATLAB functions for the
design, training, and simulation ofdesign, training, and simulation of
neural networks. It supports a wideneural networks. It supports a wide
range of network architectures with anrange of network architectures with an
unlimited number of processingunlimited number of processing
elements and interconnections (up toelements and interconnections (up to
operating system constraints).operating system constraints).

Supported architectures and trainingSupported architectures and training
methods include: supervised trainingmethods include: supervised training
of feedforward networks using theof feedforward networks using the
perceptron learning rule, Widrow-Hoffperceptron learning rule, Widrow-Hoff
rule, several variations onrule, several variations on
backpropagation (including the fastbackpropagation (including the fast
Levenberg-Marquardt algorithm), andLevenberg-Marquardt algorithm), and
radial basis networks;radial basis networks;

supervised training of recurrent Elmansupervised training of recurrent Elman
networks; unsupervised training ofnetworks; unsupervised training of
associative networks includingassociative networks including
competitive and feature map layers;competitive and feature map layers;
Kohonen networks, self-organizingKohonen networks, self-organizing
maps, and learning vectormaps, and learning vector
quantization. The Neural Networkquantization. The Neural Network
Toolbox contains a textbook-qualityToolbox contains a textbook-quality
Users' Guide, uses tutorials, referenceUsers' Guide, uses tutorials, reference
materials and sample applications withmaterials and sample applications with
code examples to explain the designcode examples to explain the design
and use of each network architectureand use of each network architecture

The Toolbox is delivered as MATLAB M-The Toolbox is delivered as MATLAB M-
files, enabling users to see thefiles, enabling users to see the
algorithms and implementations, asalgorithms and implementations, as
well as to make changes or create newwell as to make changes or create new
functions to address a specificfunctions to address a specific
application. (Comment by Richardapplication. (Comment by Richard
Andrew Miles Outerbridge,Andrew Miles Outerbridge,
RAMO@UVPHYS.PHYS.UVIC.CA:)RAMO@UVPHYS.PHYS.UVIC.CA:)
Matlab is spreading like hotcakes (andMatlab is spreading like hotcakes (and
the educational discounts are verythe educational discounts are very
impressive).impressive).

The newest release of Matlab (4.0)The newest release of Matlab (4.0)
ansrwers the question "if you couldansrwers the question "if you could
only program in one language whatonly program in one language what
would it be?". The neural networkwould it be?". The neural network
toolkit is worth getting for the manualtoolkit is worth getting for the manual
alone. Matlab is available with lots ofalone. Matlab is available with lots of
other toolkits (signal processing,other toolkits (signal processing,
optimization, etc.) but I don't use themoptimization, etc.) but I don't use them
much - the main package is more thanmuch - the main package is more than
enough. The nice thing about theenough. The nice thing about the
Matlab approach is that you can easilyMatlab approach is that you can easily
interface the neural network stuff withinterface the neural network stuff with

6. Propagator +++++++++++++ Contact:6. Propagator +++++++++++++ Contact:
ARD Corporation, 9151 Rumsey Road,ARD Corporation, 9151 Rumsey Road,
Columbia, MD 21045, USAColumbia, MD 21045, USA
propagator@ard.com Easy to usepropagator@ard.com Easy to use
neural network training package. Aneural network training package. A
GUI implementation ofGUI implementation of
backpropagation networks with fivebackpropagation networks with five
layers (32,000 nodes per layer).layers (32,000 nodes per layer).
Features dynamic performanceFeatures dynamic performance
graphs, training with a validation set,graphs, training with a validation set,
and C/C++ source code generation.and C/C++ source code generation.
For Sun (Solaris 1.x & 2.x, $499), PCFor Sun (Solaris 1.x & 2.x, $499), PC
(Windows 3.x, $199) Mac (System 7.x,(Windows 3.x, $199) Mac (System 7.x,
$199)$199)

Floating point coprocessor required,Floating point coprocessor required,
Educational Discount, Money BackEducational Discount, Money Back
Guarantee, Muliti User DiscountGuarantee, Muliti User Discount
Windows Demo on: nic.funet.fiWindows Demo on: nic.funet.fi
/pub/msdos/windows/demo/pub/msdos/windows/demo
oak.oakland.eduoak.oakland.edu
/pub/msdos/neural_nets gatordem.zip/pub/msdos/neural_nets gatordem.zip
pkzip 2.04g archive file gatordem.txtpkzip 2.04g archive file gatordem.txt
readme text filereadme text file

7. NeuroForecaster ++++++++++++++++7. NeuroForecaster ++++++++++++++++
++ Name:++ Name:
NeuroForecaster(TM)/Genetica 3.1NeuroForecaster(TM)/Genetica 3.1
Contact: Accel Infotech (S) Pte Ltd;Contact: Accel Infotech (S) Pte Ltd;
648 Geylang Road; Republic of648 Geylang Road; Republic of
Singapore 1438; Phone: +65-7446863;Singapore 1438; Phone: +65-7446863;
Fax: +65-7492467Fax: +65-7492467
accel@solomon.technet.sg For IBM PCaccel@solomon.technet.sg For IBM PC
386/486 with mouse, or compatibles386/486 with mouse, or compatibles
MS Windows* 3.1, MS DOS 5.0 orMS Windows* 3.1, MS DOS 5.0 or
above 4 MB RAM, 5 MB availableabove 4 MB RAM, 5 MB available
harddisk space min; 3.5 inch floppyharddisk space min; 3.5 inch floppy
drive, VGA monitor or above, Mathdrive, VGA monitor or above, Math
coprocessor recommended.coprocessor recommended.

Neuroforecaster 3.1 for Windows isNeuroforecaster 3.1 for Windows is
priced at US$1199 per single userpriced at US$1199 per single user
license. Please email uslicense. Please email us
(accel@solomon.technet.sg) for order(accel@solomon.technet.sg) for order
form. More information aboutform. More information about
NeuroForecaster(TM)/Genetical mayNeuroForecaster(TM)/Genetical may
be found inbe found in ftpftp://://ftpftp..technettechnet..sgsg//TechnetTechnet
//useruser//accelaccel/nfga40./nfga40.exeexe
NeuroForecaster is a user-friendlyNeuroForecaster is a user-friendly
neural network program specificallyneural network program specifically
designed for building sophisticateddesigned for building sophisticated
and powerful forecasting and decision-and powerful forecasting and decision-
support systems (Time-Seriessupport systems (Time-Series
Forecasting, Cross-SectionalForecasting, Cross-Sectional
Classification, Indicator Analysis)Classification, Indicator Analysis)
Features:Features:

* GENETICA Net Builder Option for* GENETICA Net Builder Option for
automatic network optimization * 12automatic network optimization * 12
Neuro-Fuzzy Network Models *Neuro-Fuzzy Network Models *
Multitasking & Background TrainingMultitasking & Background Training
Mode * Unlimited Network Capacity *Mode * Unlimited Network Capacity *
Rescaled Range Analysis & HurstRescaled Range Analysis & Hurst
Exponent to Unveil Hidden MarketExponent to Unveil Hidden Market
Cycles & Check for Predictability *Cycles & Check for Predictability *
Correlation Analysis to ComputeCorrelation Analysis to Compute
Correlation Factors to Analyze theCorrelation Factors to Analyze the
Significance of IndicatorsSignificance of Indicators

* Weight Histogram to Monitor the* Weight Histogram to Monitor the
Progress of Learning * AccumulatedProgress of Learning * Accumulated
Error Analysis to Analyze the StrengthError Analysis to Analyze the Strength
of Input Indicators Its user-friendlyof Input Indicators Its user-friendly
interface allows the users to buildinterface allows the users to build
applications quickly, easily andapplications quickly, easily and
interactively, analyze the data visuallyinteractively, analyze the data visually
and see the results immediately. Theand see the results immediately. The
following example applications arefollowing example applications are
included in the package: * Creditincluded in the package: * Credit
Rating - for generating the creditRating - for generating the credit
rating of bank loan applications.rating of bank loan applications.

* Stock market 6 monthly returns* Stock market 6 monthly returns
forecast * Stock selection based onforecast * Stock selection based on
company ratios * US$ to Deutschmarkcompany ratios * US$ to Deutschmark
exchange rate forecast * US$ to Yenexchange rate forecast * US$ to Yen
exchange rate forecast * US$ to SGDexchange rate forecast * US$ to SGD
exchange rate forecast * Propertyexchange rate forecast * Property
price valuation * XOR - a classicalprice valuation * XOR - a classical
problem to show the results are betterproblem to show the results are better
than others * Chaos - Prediction ofthan others * Chaos - Prediction of
Mackey-Glass chaotic time series *Mackey-Glass chaotic time series *
SineWave - For demonstrating theSineWave - For demonstrating the

GENETICA Net Builder Option - networkGENETICA Net Builder Option - network
creation & optimization based oncreation & optimization based on
Darwinian evolution theory * BackpropDarwinian evolution theory * Backprop
Neural Networks - the most widely-Neural Networks - the most widely-
used training algorithm * Fastpropused training algorithm * Fastprop
Neural Networks - speeds up trainingNeural Networks - speeds up training
of large problems * Radial Basisof large problems * Radial Basis
Function Networks - best for patternFunction Networks - best for pattern
classification problems * Neuro-Fuzzyclassification problems * Neuro-Fuzzy
Network * Rescaled Range Analysis -Network * Rescaled Range Analysis -
computes Hurst exponents to unveilcomputes Hurst exponents to unveil
hidden cycles & check forhidden cycles & check for

8. Products of NESTOR, Inc. +++++++++8. Products of NESTOR, Inc. +++++++++
++++++++++++++++++ 530 Fifth++++++++++++++++++ 530 Fifth
Avenue; New York, NY 10036; USA;Avenue; New York, NY 10036; USA;
Tel.: 001-212-398-7955 Founders: Dr.Tel.: 001-212-398-7955 Founders: Dr.
Leon Cooper (having a Nobel Price)Leon Cooper (having a Nobel Price)
and Dr. Charles Elbaum (Brownand Dr. Charles Elbaum (Brown
University). Neural Network Models:University). Neural Network Models:
Adaptive shape and patternAdaptive shape and pattern
recognition (Restricted Coulombrecognition (Restricted Coulomb
Energy - RCE) developed by NESTOREnergy - RCE) developed by NESTOR
is one of the most powerfull Neuralis one of the most powerfull Neural
Network Model used in a laterNetwork Model used in a later
products. The basis for NESTORproducts. The basis for NESTOR
products is the Nestor Learningproducts is the Nestor Learning
System -System -

NLS. Later are developed: CharacterNLS. Later are developed: Character
Learning System - CLS and ImageLearning System - CLS and Image
Learning System - ILS. NestorLearning System - ILS. Nestor
Development System - NDS is aDevelopment System - NDS is a
development tool in Standard C - onedevelopment tool in Standard C - one
of the most powerfull PC-Tools forof the most powerfull PC-Tools for
simulation and development of Neuralsimulation and development of Neural
Networks. NLS is a multi-layer, feedNetworks. NLS is a multi-layer, feed
forward system with low connectivityforward system with low connectivity
within each layer and no relaxationwithin each layer and no relaxation
procedure used for determining anprocedure used for determining an
output response. This uniqueoutput response. This unique
architecture allows the NLS to operatearchitecture allows the NLS to operate
in real time without the need forin real time without the need for

NLS is composed of multiple neuralNLS is composed of multiple neural
networks, each specializing in anetworks, each specializing in a
subset of information about the inputsubset of information about the input
patterns. The NLS integrates thepatterns. The NLS integrates the
responses of its several parallelresponses of its several parallel
networks to produce a systemnetworks to produce a system
response that is far superior to that ofresponse that is far superior to that of
other neural networks. Minimizedother neural networks. Minimized
connectivity within each layer resultsconnectivity within each layer results
in rapid training and efficient memoryin rapid training and efficient memory
utilization- ideal for current VLSIutilization- ideal for current VLSI
technology. Intel has made such atechnology. Intel has made such a

9. NeuroShell2/NeuroWindows +++++++9. NeuroShell2/NeuroWindows +++++++
++++++++++++++++++++ NeuroShell++++++++++++++++++++ NeuroShell
2 combines powerful neural network2 combines powerful neural network
architectures, a Windows icon drivenarchitectures, a Windows icon driven
user interface, and sophisticateduser interface, and sophisticated
utilities for MS-Windows machines.utilities for MS-Windows machines.
Internal format is spreadsheet, andInternal format is spreadsheet, and
users can specify that NeuroShell 2users can specify that NeuroShell 2
use their own spreadsheet whenuse their own spreadsheet when
editing. Includes both Beginner's andediting. Includes both Beginner's and
Advanced systems, a RuntimeAdvanced systems, a Runtime
capability, and a choice of 15capability, and a choice of 15
Backpropagation, Kohonen, PNN andBackpropagation, Kohonen, PNN and
GRNN architectures. Includes Rules,GRNN architectures. Includes Rules,
Symbol Translate, Graphics, FileSymbol Translate, Graphics, File
Import/Export modules (includingImport/Export modules (including

Market Technical Indicator OptionMarket Technical Indicator Option
($295), Market Technical Indicator($295), Market Technical Indicator
Option with Optimizer ($590), andOption with Optimizer ($590), and
Race Handicapping Option ($149).Race Handicapping Option ($149).
NeuroShell price: $495.NeuroShell price: $495.
NeuroWindows is a programmer's toolNeuroWindows is a programmer's tool
in a Dynamic Link Library (DLL) thatin a Dynamic Link Library (DLL) that
can create as many as 128 interactivecan create as many as 128 interactive
nets in an application, each with 32nets in an application, each with 32
slabs in a single network, and 32Kslabs in a single network, and 32K
neurons in a slab. Includesneurons in a slab. Includes
Backpropagation, Kohonen, PNN, andBackpropagation, Kohonen, PNN, and

NeuroWindows can mix supervised andNeuroWindows can mix supervised and
unsupervised nets. The DLL may beunsupervised nets. The DLL may be
called from Visual Basic, Visual C,called from Visual Basic, Visual C,
Access Basic, C, Pascal, andAccess Basic, C, Pascal, and
VBA/Excel 5. NeuroWindows price:VBA/Excel 5. NeuroWindows price:
$369. Contact: Ward Systems Group,$369. Contact: Ward Systems Group,
Inc.; Executive Park West; 5 HillcrestInc.; Executive Park West; 5 Hillcrest
Drive; Frederick, MD 21702; USA;Drive; Frederick, MD 21702; USA;
Phone: 301 662-7950; FAX: 301 662-Phone: 301 662-7950; FAX: 301 662-
5666. Contact us for a free demo5666. Contact us for a free demo
diskette and Consumer's Guide todiskette and Consumer's Guide to
Neural Networks.Neural Networks.

10. NuTank ++++++++++ NuTank stands10. NuTank ++++++++++ NuTank stands
for NeuralTank. It is educational andfor NeuralTank. It is educational and
entertainment software. In thisentertainment software. In this
program one is given the shell of a 2program one is given the shell of a 2
dimentional robotic tank. The tank hasdimentional robotic tank. The tank has
various I/O devices like wheels,various I/O devices like wheels,
whiskers, optical sensors, smell, fuelwhiskers, optical sensors, smell, fuel
level, sound and such. These I/Olevel, sound and such. These I/O
sensors are connected to Neurons.sensors are connected to Neurons.
The player/designer uses moreThe player/designer uses more
Neurons to interconnect the I/ONeurons to interconnect the I/O
devices. One can have any level ofdevices. One can have any level of
complexity desired (memory limited)complexity desired (memory limited)
and do subsumptive designs. Moreand do subsumptive designs. More
complex design take slightly more fuel,complex design take slightly more fuel,
so life is not free. All movement costsso life is not free. All movement costs

This allows neurons to learn. TheThis allows neurons to learn. The
Neuron editor can handle 3 dimentionNeuron editor can handle 3 dimention
arrays of neurons as single entitiesarrays of neurons as single entities
with very flexible interconect patterns.with very flexible interconect patterns.
One can then design a scenario withOne can then design a scenario with
walls, rocks, lights, fat (fuel) sourceswalls, rocks, lights, fat (fuel) sources
(that can be smelled) and many other(that can be smelled) and many other
such things. Robot tanks are thensuch things. Robot tanks are then
introduced into the Scenario andintroduced into the Scenario and
allowed interact or battle it out. Theallowed interact or battle it out. The
last one alive wins, or maybe one justlast one alive wins, or maybe one just
watches the motion of the robots forwatches the motion of the robots for

The entire program is mouse andThe entire program is mouse and
graphicly based. It uses DOS and VGAgraphicly based. It uses DOS and VGA
and is written in TurboC++. There willand is written in TurboC++. There will
also be the ability to download designsalso be the ability to download designs
to another computer and source codeto another computer and source code
will be available for the core neuralwill be available for the core neural
simulator. This will allow one to designsimulator. This will allow one to design
neural systems and download them toneural systems and download them to
real robots. The design tools canreal robots. The design tools can
handle three dimentional networks sohandle three dimentional networks so
will work with video camera inputs andwill work with video camera inputs and
such.such.

Eventualy I expect to do a port to UNIXEventualy I expect to do a port to UNIX
and multi thread the sign. I alsoand multi thread the sign. I also
expect to do a Mac port and maybe NTexpect to do a Mac port and maybe NT
or OS/2 Copies of NuTank cost $50or OS/2 Copies of NuTank cost $50
each. Contact: Richard Keene; Keeneeach. Contact: Richard Keene; Keene
Educational Software;Educational Software;
Dick.Keene@Central.Sun.COMDick.Keene@Central.Sun.COM
NuTank shareware with the SaveNuTank shareware with the Save
options disabled is available viaoptions disabled is available via
anonymous ftp from the Internet, seeanonymous ftp from the Internet, see
the file /pub/incoming/nutank.readmethe file /pub/incoming/nutank.readme
on the host cher.media.mit.edu.on the host cher.media.mit.edu.

11. Neuralyst +++++++++++++ Name:11. Neuralyst +++++++++++++ Name:
Neuralyst Version 1.4; Company:Neuralyst Version 1.4; Company:
Cheshire Engineering Corporation;Cheshire Engineering Corporation;
Address: 650 Sierra Madre Villa, SuiteAddress: 650 Sierra Madre Villa, Suite
201, Pasedena CA 91107; Phone: 818-201, Pasedena CA 91107; Phone: 818-
351-0209; Fax: 818-351-8645; Basic351-0209; Fax: 818-351-8645; Basic
capabilities: training ofcapabilities: training of
backpropogation neural nets.backpropogation neural nets.
Operating system: Windows orOperating system: Windows or
Macintosh running Microsoft ExcelMacintosh running Microsoft Excel
Spreadsheet. Neuralyst is an add-inSpreadsheet. Neuralyst is an add-in
package for Excel. Approx. price: $195package for Excel. Approx. price: $195
for windows or Mac. Comments: Afor windows or Mac. Comments: A
simple model that is easy to use.simple model that is easy to use.
Integrates nicely into Microsoft Excel.Integrates nicely into Microsoft Excel.

Allows user to create, train, and runAllows user to create, train, and run
backprop ANN models entirely withinbackprop ANN models entirely within
an Excel spreadsheet. Provides macroan Excel spreadsheet. Provides macro
functions that can be called from Excelfunctions that can be called from Excel
macro's, allowing you to build amacro's, allowing you to build a
custom Window's interface usingcustom Window's interface using
Excel's macro language and VisualExcel's macro language and Visual
Basic tools. The new version 1.4Basic tools. The new version 1.4
includes a genetic algorithm to guideincludes a genetic algorithm to guide
the training process. A good bargain tothe training process. A good bargain to
boot. (Comments by Duane Highley, aboot. (Comments by Duane Highley, a
user and NOT the program developer.user and NOT the program developer.

12. NeuFuz4 +++++++++++ Name:12. NeuFuz4 +++++++++++ Name:
NeuFuz4 Company: NationalNeuFuz4 Company: National
Semiconductor Corporation Address:Semiconductor Corporation Address:
2900 Semiconductor Drive, Santa2900 Semiconductor Drive, Santa
Clara, CA, 95052, or: IndustriestrasseClara, CA, 95052, or: Industriestrasse
10, D-8080 Fuerstenfeldbruck,10, D-8080 Fuerstenfeldbruck,
Germany, or: Sumitomo ChemicalGermany, or: Sumitomo Chemical
Engineering Center, Bldg. 7F 1-7-1,Engineering Center, Bldg. 7F 1-7-1,
Nakase, Mihama-Ku, Chiba-City, CibaNakase, Mihama-Ku, Chiba-City, Ciba
Prefecture 261, JAPAN, or: 15th Floor,Prefecture 261, JAPAN, or: 15th Floor,
Straight Block, Ocean Centre, 5Straight Block, Ocean Centre, 5
Canton Road, Tsim Sha Tsui East,Canton Road, Tsim Sha Tsui East,
Kowloon, Hong Kong, Phone: (800)Kowloon, Hong Kong, Phone: (800)
272-9959 (Americas), : 011-49-8141-272-9959 (Americas), : 011-49-8141-
103-0103-0

Germany : 0l1-81-3-3299-7001 Japan :Germany : 0l1-81-3-3299-7001 Japan :
(852) 737-1600 Hong Kong Email:(852) 737-1600 Hong Kong Email:
neufuz@esd.nsc.com (Neural netneufuz@esd.nsc.com (Neural net
inquiries only) URL:inquiries only) URL:
http://www.commerce.net/directories/phttp://www.commerce.net/directories/p
articipants/ns/home.html Basicarticipants/ns/home.html Basic
capabilities: Uses backpropagationcapabilities: Uses backpropagation
techniques to initially select fuzzytechniques to initially select fuzzy
rules and membership functions.rules and membership functions.

The result is a fuzzy associative memoryThe result is a fuzzy associative memory
(FAM) which implements an(FAM) which implements an
approximation of the training data.approximation of the training data.
Operating Systems: 486DX-25 orOperating Systems: 486DX-25 or
higher with math co-processor DOShigher with math co-processor DOS
5.0 or higher with Windows 3.1,5.0 or higher with Windows 3.1,
mouse, VGA or better, minimum 4 MBmouse, VGA or better, minimum 4 MB
RAM, and parallel port. Approx. price :RAM, and parallel port. Approx. price :
depends on version - see below.depends on version - see below.
Comments : Not for the serious NeuralComments : Not for the serious Neural
Network researcher, but good for aNetwork researcher, but good for a
person who has little understanding ofperson who has little understanding of

The systems are aimed at low endThe systems are aimed at low end
controls applications in automotive,controls applications in automotive,
industrial, and appliance areas.industrial, and appliance areas.
NeuFuz is a neural-fuzzy technologyNeuFuz is a neural-fuzzy technology
which uses backpropagationwhich uses backpropagation
techniques to initially select fuzzytechniques to initially select fuzzy
rules and membership functions. Initialrules and membership functions. Initial
stages of design using NeuFuzstages of design using NeuFuz
technology are performed usingtechnology are performed using
training data and backpropagation.training data and backpropagation.

The result is a fuzzy associative memoryThe result is a fuzzy associative memory
(FAM) which implements an(FAM) which implements an
approximation of the training data. Byapproximation of the training data. By
implementing a FAM, rather than aimplementing a FAM, rather than a
multi-layer perceptron, the designermulti-layer perceptron, the designer
has a solution which can behas a solution which can be
understood and tuned to a particularunderstood and tuned to a particular
application using Fuzzy Logic designapplication using Fuzzy Logic design
techniques. There are several differenttechniques. There are several different
versions, some with COP8 Codeversions, some with COP8 Code
Generator (COP8 is National's familyGenerator (COP8 is National's family
of 8-bit microcontrollers) and COP8 in-of 8-bit microcontrollers) and COP8 in-

13. Cortex-Pro ++++++++++++++13. Cortex-Pro ++++++++++++++
Cortex-Pro information is onCortex-Pro information is on
WWW at:WWW at: http://http://wwwwww..neuronetneuronet..phph..
kclkcl..acac..ukuk//neuronetneuronet/software//software/
cortexcortex/www1./www1.htmlhtml. You can. You can
download a working demo fromdownload a working demo from
there. Contact: Michael Reiss (there. Contact: Michael Reiss (
http://http://wwwwww..mthmth..kclkcl..acac..ukuk/~/~mreissmreiss//
mickmick..htmlhtml) email:) email:
<m.reiss@kcl.ac.uk>.<m.reiss@kcl.ac.uk>.

14. PARTEK ++++++++++ PARTEK is a14. PARTEK ++++++++++ PARTEK is a
powerful, integrated environment forpowerful, integrated environment for
visual and quantitative data analysisvisual and quantitative data analysis
and pattern recognition. Drawing fromand pattern recognition. Drawing from
a wide variety of disciplines includinga wide variety of disciplines including
Artificial Neural Networks, FuzzyArtificial Neural Networks, Fuzzy
Logic, Genetic Algorithms, andLogic, Genetic Algorithms, and
Statistics, PARTEK integrates dataStatistics, PARTEK integrates data
analysis and modeling tools into ananalysis and modeling tools into an
easy to use "point and click" system.easy to use "point and click" system.
The following modules are availableThe following modules are available
from PARTEK; functions from differentfrom PARTEK; functions from different
modules are integrated with eachmodules are integrated with each
other whereever possible:other whereever possible:

1. The PARTEK/AVB - The1. The PARTEK/AVB - The
Analytical/Visual Base. (TM) *Analytical/Visual Base. (TM) *
Analytical Spreadsheet (TM) TheAnalytical Spreadsheet (TM) The
Analytical Spreadsheet is a powerfulAnalytical Spreadsheet is a powerful
and easy to use data analysis,and easy to use data analysis,
transformations, and visualization tool.transformations, and visualization tool.
Some features include: - import nativeSome features include: - import native
format ascii/binary data - recognitionformat ascii/binary data - recognition
and resolution of missing data -and resolution of missing data -
complete set of common mathematicalcomplete set of common mathematical
& statistical functions -& statistical functions -

contingency table analysis /contingency table analysis /
correspondence analysis - univariatecorrespondence analysis - univariate
histogram analysis - extensive set ofhistogram analysis - extensive set of
smoothing and normalizationsmoothing and normalization
transformations - easily and quicklytransformations - easily and quickly
plot color-coded 1-D curves andplot color-coded 1-D curves and
histograms, 2-D, 3-D, and N-D mappedhistograms, 2-D, 3-D, and N-D mapped
scatterplots, highlighting selectedscatterplots, highlighting selected
patterns - Command Line (Tcl) andpatterns - Command Line (Tcl) and
Graphical Interface * PatternGraphical Interface * Pattern
Visualization System (TM) The PatternVisualization System (TM) The Pattern
Visualization System offers the mostVisualization System offers the most
powerful tools for visual analysis ofpowerful tools for visual analysis of
the patterns in your data. Somethe patterns in your data. Some
features include: -features include: -

automatically maps N-D data down to 3-automatically maps N-D data down to 3-
D for visualization of *all* of yourD for visualization of *all* of your
variables at once - hard copy colorvariables at once - hard copy color
Postscript output - a variety of color-Postscript output - a variety of color-
coding, highlighting, and labelingcoding, highlighting, and labeling
options allow you to generateoptions allow you to generate
meaningful graphics * Data Filtersmeaningful graphics * Data Filters
Filter out selected rows and/orFilter out selected rows and/or
columns of your data for flexible andcolumns of your data for flexible and
efficient cross-validation, jackknifing,efficient cross-validation, jackknifing,
bootstrapping, feature set evaluation,bootstrapping, feature set evaluation,
and more. * Random # Generatorsand more. * Random # Generators
Generate random numbers from any ofGenerate random numbers from any of
the following parameterizedthe following parameterized
distributions: - uniform, normal,distributions: - uniform, normal,
exponential, gamma, binomial, poissonexponential, gamma, binomial, poisson

* Many distance/similarity metrics* Many distance/similarity metrics
Choose the appropriate distanceChoose the appropriate distance
metric for your data: - euclidean,metric for your data: - euclidean,
mahalanobis, minkowski, maximummahalanobis, minkowski, maximum
value, absolute value, shapevalue, absolute value, shape
coefficient, cosine coefficient, pearsoncoefficient, cosine coefficient, pearson
correlation, rank correlation, kendall'scorrelation, rank correlation, kendall's
tau, canberra, and bray-curtis * Tcl/Tktau, canberra, and bray-curtis * Tcl/Tk
command line interface 2. Thecommand line interface 2. The
PARTEK/DSA - Data StructurePARTEK/DSA - Data Structure
Analysis Module * PrincipalAnalysis Module * Principal
Components Analysis and RegressionComponents Analysis and Regression
Also known as Eigenvector ProjectionAlso known as Eigenvector Projection
or Karhunen-Loeve Expansions, PCAor Karhunen-Loeve Expansions, PCA
removes redundant information fromremoves redundant information from
your data.your data.

- component analysis, correlate PC's- component analysis, correlate PC's
with original variables - choice ofwith original variables - choice of
covariance, correlation, or productcovariance, correlation, or product
dispersion matrices - choice ofdispersion matrices - choice of
eigenvector, y-score, and z-scoreeigenvector, y-score, and z-score
projections - view SCREE and log-projections - view SCREE and log-
eigenvalue plots * Cluster Analysiseigenvalue plots * Cluster Analysis
Does the data form groups? HowDoes the data form groups? How
many? How compact? Cluster Analysismany? How compact? Cluster Analysis
is the tool to answer these questions. -is the tool to answer these questions. -
choose between several distancechoose between several distance
metrics - optionally weight individualmetrics - optionally weight individual
patterns - manually or auto-select thepatterns - manually or auto-select the
cluster number and initial centers -cluster number and initial centers -

cluster labeled data to a matrix viewercluster labeled data to a matrix viewer
or the Analytical Spreadsheet foror the Analytical Spreadsheet for
further analysis - visualize n-further analysis - visualize n-
dimensional clustering - assessdimensional clustering - assess
goodness of partion using severalgoodness of partion using several
internal and external criteria metrics *internal and external criteria metrics *
N-Dimensional Histogram AnalysisN-Dimensional Histogram Analysis
Among the most inportant questions aAmong the most inportant questions a
researcher needs to know whenresearcher needs to know when
analyzing patterns is whether or notanalyzing patterns is whether or not
the patterns can distinguish differentthe patterns can distinguish different
classes of data. N-D Histogramclasses of data. N-D Histogram
Analysis is one tool to answer thisAnalysis is one tool to answer this
question. - measures histogramquestion. - measures histogram
overlap in n-dimensional space -overlap in n-dimensional space -
automatically find the best subset ofautomatically find the best subset of

Non-Linear Mapping NLM is an iterativeNon-Linear Mapping NLM is an iterative
algorithm for visually analyzing thealgorithm for visually analyzing the
structure of n-dimensional data. NLMstructure of n-dimensional data. NLM
produces a non-linear mapping of dataproduces a non-linear mapping of data
which preserves interpoint distanceswhich preserves interpoint distances
of n-dimensional data while reducingof n-dimensional data while reducing
to a lower dimensionality - thusto a lower dimensionality - thus
preserving the structure of the data. -preserving the structure of the data. -
visually analyze structure of n-visually analyze structure of n-
dimensional data - track progress withdimensional data - track progress with
error curves - orthogonal, PCA, anderror curves - orthogonal, PCA, and
random initialization 3.random initialization 3.

The PARTEK/CP - Classification andThe PARTEK/CP - Classification and
Prediction Module. * Multi-LayerPrediction Module. * Multi-Layer
Perceptron The most popular amongPerceptron The most popular among
the neural pattern recognition tools isthe neural pattern recognition tools is
the MLP. PARTEK takes the MLP to athe MLP. PARTEK takes the MLP to a
new dimension, by allowing thenew dimension, by allowing the
network to learn by adapting ALL of itsnetwork to learn by adapting ALL of its
parameters to solve a problem. -parameters to solve a problem. -
adapts output bias, neuron activationadapts output bias, neuron activation
steepness, and neuron dynamic range,steepness, and neuron dynamic range,
as well as weights and input biases -as well as weights and input biases -
auto-scaling at input and output - noauto-scaling at input and output - no
need to rescale your data -need to rescale your data -

choose between sigmoid, gaussian,choose between sigmoid, gaussian,
linear, or mixture of neurons - learninglinear, or mixture of neurons - learning
rate, momentum can be setrate, momentum can be set
independently for each parameter -independently for each parameter -
variety of learning methods andvariety of learning methods and
network initializations - view color-network initializations - view color-
coded network, error, etc as networkcoded network, error, etc as network
trains, tests, runs * Learning Vectortrains, tests, runs * Learning Vector
Quantization Because LVQ is aQuantization Because LVQ is a
multiple prototype classifier, it adaptsmultiple prototype classifier, it adapts
to identify multiple sub-groups withinto identify multiple sub-groups within
classes -classes -

LVQ1, LVQ2, and LVQ3 trainingLVQ1, LVQ2, and LVQ3 training
methods - 3 different functions formethods - 3 different functions for
adapting learning rate - chooseadapting learning rate - choose
between several distance metrics -between several distance metrics -
fuzzy and crisp classifications - setfuzzy and crisp classifications - set
number of prototypes individually fornumber of prototypes individually for
each class * Bayesian Classifier Bayeseach class * Bayesian Classifier Bayes
methods are the statistical decisionmethods are the statistical decision
theory approach to classification. Thistheory approach to classification. This
classifier uses statistical properties ofclassifier uses statistical properties of
your data to develop a classificationyour data to develop a classification
model. PARTEK is available on HP,model. PARTEK is available on HP,
IBM, Silicon Graphics, and SUNIBM, Silicon Graphics, and SUN
workstations. For more information,workstations. For more information,
send email to "info@partek.com" orsend email to "info@partek.com" or

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