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 GalleryBackBrainBackBrainBackprop-1.4Backprop-1.4bpsbpsFuNeGenFuNeGenHyperplane AnimatorHyperplane AnimatorLVQ PAKLVQ PAK

NETS NETS NeuralShellNeuralShellNeuDLNeuDLNeurfuzzNeurfuzzNeuroForecaster/GANeuroForecaster/GANeuroSolutionsNeuroSolutionsNevPropNevProp


Simuladores de RNASimuladores de RNA

NICO NICO nn/xnnnn/xnnPDP Software PDP Software PittnetPittnetSOM PAKSOM PAKSPIDER Web Neural SPIDER Web Neural Network LibraryNetwork Library

TDNNTDNNtlearntlearnWinNN WinNN Xerion SimulatorXerion Simulator

Neural Network Neural Network Toolbox Toolbox


The ART GalleryThe ART Gallery

Descripción: ART Gallery es una serie de Descripción: ART Gallery es una serie de procedimientos dedicados a ser usados procedimientos dedicados a ser usados con otros codigos para implementar redes con 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 tipo Descripción: BackBrain simula redes de tipo Backpropagation; permite, crear,entrenar y Backpropagation; permite, crear,entrenar y analizar redes. Tambien crea modelos en 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 Southampton Desarrollador: University of Southampton UK.UK.


Backprop-1.4Backprop-1.4

Descripción: Programa manipulado por Descripción: Programa manipulado por *Mouse* permite diseñar redes de forma *Mouse* permite diseñar redes de forma grafica; el sistema esta limitado a redes grafica; el sistema esta limitado a redes con un maximo de 25 neuronas . Fue con un maximo de 25 neuronas . Fue desarrollado con el proposito de desarrollado con el proposito de aprendizaje de redes Backpropagation.aprendizaje de redes Backpropagation.

Plataforma: DOSPlataforma: DOS

Desarrollador: University of Kassel Desarrollador: University of Kassel


bpsbps

Descripción: Sistema para el desarrollo de Descripción: Sistema para el desarrollo de redes entrenadas por el algoritmo de redes entrenadas por el algoritmo de retropropagación de error.retropropagación de error.

Plataformas: PC, VAX, MAC.Plataformas: PC, VAX, MAC.

Desarrollador: Eugene Norris, Computer Desarrollador: Eugene Norris, Computer Science Deparment; Georgr Mason Science Deparment; Georgr Mason University, Virginia USA. University, Virginia USA.


FuNeGenFuNeGen

Descripción: Esta basado en los conceptos Descripción: Esta basado en los conceptos de sistemas neurodifusos, puede generar de sistemas neurodifusos, puede generar sistemas de clasificación difusa de sistemas de clasificación difusa de información muestreada, no hay limitantes información muestreada, no hay limitantes en cuanto al numero de entradas y salidas; en cuanto al numero de entradas y salidas; además permite eliminar entradas además 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 un Descripción: Hyperplane Animator es un programa que permite fácilmente de programa que permite fácilmente de manera gráfica el entrenamiento de redes manera 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 metodos Descripción: Es un grupo de metodos aplicables al reconocimiento estadistico de aplicables al reconocimiento estadistico de patrones, en las cuales las clases son patrones, en las cuales las clases son descritas por un numero relativamente descritas por un numero relativamente pequeño de vectores codigo.pequeño de vectores codigo.

Desarrollador: Teuvo Kohonen, Helsinki Desarrollador: Teuvo Kohonen, Helsinki University of Technology; FinlandiaUniversity of Technology; Finlandia


NETS NETS

Descripción: Network Execution and Training Descripción: Network Execution and Training Simulator (NETS) Es una herramienta la cual Simulator (NETS) Es una herramienta la cual proporciona un ambiente para el desarrollo y proporciona un ambiente para el desarrollo y evaluación de redes neuronales. El sistema evaluación de redes neuronales. El sistema permite crear y ejecutar configuraciones permite crear y ejecutar configuraciones arbitrarias de redes las cuales usan aprendizaje arbitrarias de redes las cuales usan aprendizaje de retropropagación. de retropropagación.

Desarrollador: COSMIC, University of GeorgiaDesarrollador: COSMIC, University of Georgia


Neural Networks Neural Networks at your Firgertipsat your Firgertips

Descripción: simulador de las 8 mas populares Descripción: simulador de las 8 mas populares arquitecturas de redes neuronales; codigo arquitecturas 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 de Memoria 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 simuladores Descripción: 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 State Desarrollador: SPANN Laboratory, Ohio State University, columbus, USA.University, columbus, USA.


NeuroSolutionsNeuroSolutions

Descripción: Sistema consistente de un conjunto de Descripción: Sistema consistente de un conjunto de tutoriales de diferentes tipos de redes entre las tutoriales de diferentes tipos de redes entre las cuales están, Perceptron, asociador lineal, filtros cuales están, Perceptron, asociador lineal, filtros adaptativos, redes jordan-elman, Mapas de adaptativos, redes jordan-elman, Mapas de Kohonen, redes de base radial, etc. El software Kohonen, redes de base radial, etc. El software permite construir y entrenar redes neuronales permite 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 una Neural Network Description Lenguage es una nueva herramienta con un lenguaje de nueva herramienta con un lenguaje de programación interprete, dedicado a la programación interprete, dedicado a la construcción, entrenamiento, prueba y corridas construcción, 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 Alabama Alabama


NeurfuzzNeurfuzz

Descripción: Neurofuzz 1.0 es un generador Descripción: Neurofuzz 1.0 es un generador de código C para sistemas difusos y redes de código C para sistemas difusos y redes neuronales artificiales tipo neuronales artificiales tipo Backpropagation.Backpropagation.

Desarrollador: Luca Marchese.Desarrollador: Luca Marchese.


NeuroForecaster/GANeuroForecaster/GA

Descripción: NeuroForecaster/GA Versión 7.0 es Descripción: NeuroForecaster/GA Versión 7.0 es una red neuronal de 32 bits y algoritmos una red neuronal de 32 bits y algoritmos geneticos basados en programas de predicción geneticos 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 usar Descripción: NevProp es un programa fácil de usar para redes feedforward tipo perceptron para redes feedforward tipo perceptron multicapa y Back propagation. Usa una interfaz multicapa 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 NeuralNetwork ToolkitNetwork Toolkit

Descripción: Es una herramienta de desarrollo de redes Descripción: Es una herramienta de desarrollo de redes neuronales, diseñada y optimizadas para el neuronales, diseñada y optimizadas para el reconocimiento automatico de voz; se pueden construir reconocimiento automatico de voz; se pueden construir redes con conexiones recurrentes y retardos, la topologia redes con conexiones recurrentes y retardos, la topologia de las redes es muy flexible, permite cualquier numero de de las redes es muy flexible, permite cualquier numero de capas y las cuales pueden ser arbitrariamente capas y las cuales pueden ser arbitrariamente conectadas.conectadas.

Plataforma : UNIX,codigo fuente ANSI-C en :HPUX, SUN Plataforma : 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 y Descripción : nn/xnn es un sistema para el desarrollo y simulación de redes neuronales. Nn es un lenguaje de simulación 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 o dicho compilador puede generar codigo en C o programas ejecutables; al usar los modelos incluidos en programas 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 de LVQ, 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 procesos Descripción: Simulador de procesos distribuidos en paralelo.distribuidos en paralelo.

Algoritmos: Redes Feedforward y varias Algoritmos: 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 permitir sal Descripción: El proposito del sistema es permitir sal usuario construir, entrenar y probar diferentes usuario construir, entrenar y probar diferentes tipos de redes neuronales.tipos de redes neuronales.

Algoritmos: Redes Feedforward con Algoritmos: 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 Neural SpiderWeb Neural Network LibraryNetwork Library

Descripción: Codigo fuente C++ para implementar Descripción: Codigo fuente C++ para implementar redes neuronales; esta diseñado para ser redes neuronales; esta diseñado para ser facilmente extendido a aumentar sus facilmente 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 Neural Time Delay Neural Network - TDNNNetwork - TDNN

Descripción: El sistema consiste de una red con Descripción: El sistema consiste de una red con una topologia fija predefinida para el una topologia fija predefinida para el reconocimiento de digitos hablados del 0 al 9 reconocimiento de digitos hablados del 0 al 9 partiendo de voz continua, la capa de entrada partiendo 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 redes Descrpción: tlearn es un simulador de redes neuronales la cual implementa la regla de neuronales la cual implementa la regla de aprendizaje de retropropagación, incluye redes aprendizaje de retropropagación, incluye redes recurrentes simples; icluye un editor de textos y recurrentes simples; icluye un editor de textos y un gran numero de utilerias para el analisis de un 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 muy Descripción: 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 a WinNN es una herramienta que esta dedicada a usuarios principiantes y mas avanzados de redes usuarios principiantes y mas avanzados de redes neuronales. Permite implementar redes feeforward neuronales. Permite implementar redes feeforward multicapa utilizando el algoritmo de retropropagación multicapa 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 de Descripción: Xerion esta conformado por un conjunto de bibliotecas en C que pueden ser usadas para la bibliotecas en C que pueden ser usadas para la construcción de redes neuronales experimentales construcción de redes neuronales experimentales complejas, y preconstruir simuladores escritos con estas complejas, 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 Network Neural Network Toolbox (Matlab)Toolbox (Matlab)

Descripción: Herramienta para el desarrollo y Descripción: Herramienta para el desarrollo y entrenamiento de redes neuronales bajo el entrenamiento 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 packages for NN 18. A: Commercial software packages for NN simulation? simulation? =================================================================================== 1. ========================= 1. nn/xnn +++++++++ Name: nn/xnn nn/xnn +++++++++ Name: nn/xnn Company: Neureka ANS Address: Klaus Company: Neureka ANS Address: Klaus Hansens vei 31B 5037 Solheimsviken Hansens vei 31B 5037 Solheimsviken NORWAY Phone: +47-55544163 / +47-NORWAY Phone: +47-55544163 / +47-55201548 Email: [email protected] 55201548 Email: [email protected] Basic capabilities: Neural network Basic capabilities: Neural network development tool. nn is a language for development tool. nn is a language for specification of neural network simulators. specification of neural network simulators. Produces C-code and executables for the Produces C-code and executables for the specified models, therefore ideal for specified models, therefore ideal for application development. xnn is a application development. xnn is a graphical front-end to nn and the graphical front-end to nn and the simulation code produced by nn. simulation code produced by nn.


Gives graphical representations in a Gives graphical representations in a number of formats of any variables during number of formats of any variables during simulation run-time. Comes with a simulation run-time. Comes with a number of pre-implemented models, number of pre-implemented models, including: Backprop (several variants), including: Backprop (several variants), Self Organizing Maps, LVQ1, LVQ2, Radial Self Organizing Maps, LVQ1, LVQ2, Radial Basis Function Networks, Generalized Basis Function Networks, Generalized Regression Neural Networks, Jordan nets, Regression Neural Networks, Jordan nets, Elman nets, Hopfield, etc. Operating Elman nets, Hopfield, etc. Operating system: nn: UNIX or MS-DOS, xnn: UNIX/X-system: nn: UNIX or MS-DOS, xnn: UNIX/X-windows System requirements: 10 Mb HD, windows System requirements: 10 Mb HD, 2 Mb RAM Approx. price: USD 2000,- 2 Mb RAM Approx. price: USD 2000,-


2. BrainMaker +++++++++++++ 2. BrainMaker +++++++++++++ Name: BrainMaker, BrainMaker Pro Name: BrainMaker, BrainMaker Pro Company: California Scientific Company: California Scientific Software Address: 10024 Newtown rd, Software Address: 10024 Newtown rd, Nevada City, CA, 95959 USA Nevada City, CA, 95959 USA Phone,Fax: 916 478 9040, 916 478 Phone,Fax: 916 478 9040, 916 478 9041 Email: calsci!9041 Email: [email protected] [email protected] (flakey connection) Basic capabilities: (flakey connection) Basic capabilities: train backprop neural nets Operating train backprop neural nets Operating system: DOS, Windows, Mac System system: DOS, Windows, Mac System requirements: Uses XMS or EMS for requirements: Uses XMS or EMS for large models(PCs only): Pro version large models(PCs only): Pro version Approx. price: $195, $795 BrainMaker Approx. 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 version BrainMaker 2.5 Student version (quantity sales only, about $38 (quantity sales only, about $38 each) BrainMaker Pro C30 each) BrainMaker Pro C30 Accelerator Board w/ 5Mb memory Accelerator Board w/ 5Mb memory $9750 w/32Mb memory $13,000 $9750 w/32Mb memory $13,000 Intel iNNTS NN Development Intel iNNTS NN Development System $11,800System $11,800


Intel EMB Multi-Chip Board $9750 Intel 80170 Intel EMB Multi-Chip Board $9750 Intel 80170 chip set $940 Introduction To Neural chip set $940 Introduction To Neural Networks book $30 California Scientific Networks book $30 California Scientific Software can be reached at: Phone: 916 478 Software can be reached at: Phone: 916 478 9040 Fax: 916 478 9041 Tech Support: 916 9040 Fax: 916 478 9041 Tech Support: 916 478 9035 Mail: 10024 newtown rd, Nevada 478 9035 Mail: 10024 newtown rd, Nevada City, CA, 95959, USA 30 day money back City, CA, 95959, USA 30 day money back guarantee, and unlimited free technical guarantee, and unlimited free technical support. BrainMaker package includes: The support. BrainMaker package includes: The book Introduction to Neural Networks book Introduction to Neural Networks BrainMaker Users Guide and reference BrainMaker Users Guide and reference manual 300 pages , fully indexed, with manual 300 pages , fully indexed, with tutorials, and sample networks Netmakertutorials, and sample networks Netmaker


Netmaker makes building and training Neural Netmaker makes building and training Neural Networks easy, by importing and Networks easy, by importing and automatically creating BrainMaker's Neural automatically creating BrainMaker's Neural Network files. Netmaker imports Lotus, Network files. Netmaker imports Lotus, Excel, dBase, and ASCII files. BrainMaker Excel, dBase, and ASCII files. BrainMaker Full menu and dialog box interface, runs Full menu and dialog box interface, runs Backprop at 750,000 cps on a 33Mhz 486. ---Backprop at 750,000 cps on a 33Mhz 486. ---Features ("P" means is avaliable in Features ("P" means is avaliable in professional version only): Pull-down Menus, professional version only): Pull-down Menus, Dialog Boxes, Programmable Output Files, Dialog Boxes, Programmable Output Files, Editing in BrainMaker, Network Progress Editing in BrainMaker, Network Progress Display (P), Fact Annotation, supports many Display (P), Fact Annotation, supports many printers, NetPlotter, Graphics Built In (P), printers, NetPlotter, Graphics Built In (P),


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


Global Network Analysis (P), Contour Global Network Analysis (P), Contour Analysis (P), Data Correlator (P), Error Analysis (P), Data Correlator (P), Error Statistics Report, Print or Edit Weight Statistics Report, Print or Edit Weight Matrices, Competitor (P), Run Time System Matrices, Competitor (P), Run Time System (P), Chip Support for Intel, American (P), Chip Support for Intel, American Neurologics, Micro Devices, Genetic Neurologics, Micro Devices, Genetic Training Option (P), NetMaker, Training Option (P), NetMaker, NetChecker, Shuffle, Data Import from NetChecker, Shuffle, Data Import from Lotus, dBASE, Excel, ASCII, binary, Finacial Lotus, dBASE, Excel, ASCII, binary, Finacial Data (P), Data Manipulation, Cyclic Data (P), Data Manipulation, Cyclic Analysis (P), User's Guide quick start Analysis (P), User's Guide quick start booklet, Introduction to Neural Networks booklet, Introduction to Neural Networks 324 pp book 324 pp book


3. SAS Software/ Neural Net add-on +++3. SAS Software/ Neural Net add-on ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Name: SAS Software Company: +++++ Name: SAS Software Company: SAS Institute, Inc. Address: SAS SAS Institute, Inc. Address: SAS Campus Drive, Cary, NC 27513, USA Campus Drive, Cary, NC 27513, USA Phone,Fax: (919) 677-8000 Email: Phone,Fax: (919) 677-8000 Email: [email protected] (Neural net [email protected] (Neural net inquiries only) Basic capabilities: inquiries only) Basic capabilities: Feedforward nets with numerous Feedforward nets with numerous training methods and loss functions, training methods and loss functions, plus statistical analogs of plus statistical analogs of counterpropagation and various counterpropagation and various unsupervised architectures Operating unsupervised architectures Operating system: Lots System requirements: system: Lots System requirements: Lots Uses XMS or EMS for large Lots 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, but price: Free neural net software, but you have to license SAS/Base you have to license SAS/Base software and preferably the software and preferably the SAS/OR, SAS/ETS, and/or SAS/STAT SAS/OR, SAS/ETS, and/or SAS/STAT products. Comments: Oriented products. Comments: Oriented toward data analysis and statistical toward data analysis and statistical applications applications


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


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


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


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


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


The Toolbox is delivered as MATLAB The Toolbox is delivered as MATLAB M-files, enabling users to see the M-files, enabling users to see the algorithms and implementations, as algorithms and implementations, as well as to make changes or create well as to make changes or create new functions to address a specific new functions to address a specific application. (Comment by Richard application. (Comment by Richard Andrew Miles Outerbridge, Andrew Miles Outerbridge, [email protected]:) [email protected]:) Matlab is spreading like hotcakes Matlab is spreading like hotcakes (and the educational discounts are (and the educational discounts are very impressive). very impressive).


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


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


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


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


Neuroforecaster 3.1 for Windows is Neuroforecaster 3.1 for Windows is priced at US$1199 per single user priced at US$1199 per single user license. Please email us license. Please email us ([email protected]) for order ([email protected]) for order form. More information about form. More information about NeuroForecaster(TM)/Genetical may be NeuroForecaster(TM)/Genetical may be found in found in ftpftp://://ftpftp..technettechnet..sgsg//TechnetTechnet//useruser//accelaccel/nfga40./nfga40.exeexe NeuroForecaster NeuroForecaster is a user-friendly neural network is a user-friendly neural network program specifically designed for program specifically designed for building sophisticated and powerful building sophisticated and powerful forecasting and decision-support forecasting and decision-support systems (Time-Series Forecasting, systems (Time-Series Forecasting, Cross-Sectional Classification, Cross-Sectional Classification, Indicator Analysis) Features:Indicator Analysis) Features:


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


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


* Stock market 6 monthly returns forecast * * Stock market 6 monthly returns forecast * Stock selection based on company ratios * Stock selection based on company ratios * US$ to Deutschmark exchange rate forecast US$ to Deutschmark exchange rate forecast * US$ to Yen exchange rate forecast * US$ * US$ to Yen exchange rate forecast * US$ to SGD exchange rate forecast * Property to SGD exchange rate forecast * Property price valuation * XOR - a classical problem price valuation * XOR - a classical problem to show the results are better than others * to show the results are better than others * Chaos - Prediction of Mackey-Glass chaotic Chaos - Prediction of Mackey-Glass chaotic time series * SineWave - For demonstrating time series * SineWave - For demonstrating the power of Rescaled Range Analysis and the power of Rescaled Range Analysis and significance of window size Techniques significance of window size Techniques Implemented: *Implemented: *


GENETICA Net Builder Option - network GENETICA Net Builder Option - network creation & optimization based on Darwinian creation & optimization based on Darwinian evolution theory * Backprop Neural evolution theory * Backprop Neural Networks - the most widely-used training Networks - the most widely-used training algorithm * Fastprop Neural Networks - algorithm * Fastprop Neural Networks - speeds up training of large problems * speeds up training of large problems * Radial Basis Function Networks - best for Radial Basis Function Networks - best for pattern classification problems * Neuro-pattern classification problems * Neuro-Fuzzy Network * Rescaled Range Analysis - Fuzzy Network * Rescaled Range Analysis - computes Hurst exponents to unveil hidden computes Hurst exponents to unveil hidden cycles & check for predictability * cycles & check for predictability * Correlation Analysis - to identify significant Correlation Analysis - to identify significant input indicators input indicators


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


NLS. Later are developed: Character Learning NLS. Later are developed: Character Learning System - CLS and Image Learning System - ILS. System - CLS and Image Learning System - ILS. Nestor Development System - NDS is a Nestor Development System - NDS is a development tool in Standard C - one of the development tool in Standard C - one of the most powerfull PC-Tools for simulation and most powerfull PC-Tools for simulation and development of Neural Networks. NLS is a multi-development of Neural Networks. NLS is a multi-layer, feed forward system with low connectivity layer, feed forward system with low connectivity within each layer and no relaxation procedure within each layer and no relaxation procedure used for determining an output response. This used for determining an output response. This unique architecture allows the NLS to operate in unique architecture allows the NLS to operate in real time without the need for special real time without the need for special computers or custom hardware.computers or custom hardware.


NLS is composed of multiple neural NLS is composed of multiple neural networks, each specializing in a subset networks, each specializing in a subset of information about the input patterns. of information about the input patterns. The NLS integrates the responses of its The NLS integrates the responses of its several parallel networks to produce a several parallel networks to produce a system response that is far superior to system response that is far superior to that of other neural networks. that of other neural networks. Minimized connectivity within each Minimized connectivity within each layer results in rapid training and layer results in rapid training and efficient memory utilization- ideal for efficient memory utilization- ideal for current VLSI technology. Intel has made current VLSI technology. Intel has made such a chip - NE1000. such a chip - NE1000.


9. NeuroShell2/NeuroWindows +++++++++9. NeuroShell2/NeuroWindows +++++++++++++++++++++++++++ NeuroShell 2 ++++++++++++++++++ NeuroShell 2 combines powerful neural network combines powerful neural network architectures, a Windows icon driven user architectures, a Windows icon driven user interface, and sophisticated utilities for interface, and sophisticated utilities for MS-Windows machines. Internal format is MS-Windows machines. Internal format is spreadsheet, and users can specify that spreadsheet, and users can specify that NeuroShell 2 use their own spreadsheet NeuroShell 2 use their own spreadsheet when editing. Includes both Beginner's and when editing. Includes both Beginner's and Advanced systems, a Runtime capability, Advanced systems, a Runtime capability, and a choice of 15 Backpropagation, and a choice of 15 Backpropagation, Kohonen, PNN and GRNN architectures. Kohonen, PNN and GRNN architectures. Includes Rules, Symbol Translate, Graphics, Includes Rules, Symbol Translate, Graphics, File Import/Export modules (including File Import/Export modules (including MetaStock from Equis International) and MetaStock from Equis International) and NET-PERFECT to prevent overtraining. NET-PERFECT to prevent overtraining. Options available:Options available:


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


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


10. NuTank ++++++++++ NuTank stands for 10. NuTank ++++++++++ NuTank stands for NeuralTank. It is educational and NeuralTank. It is educational and entertainment software. In this program entertainment software. In this program one is given the shell of a 2 dimentional one is given the shell of a 2 dimentional robotic tank. The tank has various I/O robotic tank. The tank has various I/O devices like wheels, whiskers, optical devices like wheels, whiskers, optical sensors, smell, fuel level, sound and such. sensors, smell, fuel level, sound and such. These I/O sensors are connected to These I/O sensors are connected to Neurons. The player/designer uses more Neurons. The player/designer uses more Neurons to interconnect the I/O devices. Neurons to interconnect the I/O devices. One can have any level of complexity One can have any level of complexity desired (memory limited) and do desired (memory limited) and do subsumptive designs. More complex design subsumptive designs. More complex design take slightly more fuel, so life is not free. take slightly more fuel, so life is not free. All movement costs fuel too. One can also All movement costs fuel too. One can also tag neuron connections as "adaptable" that tag neuron connections as "adaptable" that adapt their weights in acordance with the adapt their weights in acordance with the target neuron. target neuron.


This allows neurons to learn. The Neuron This allows neurons to learn. The Neuron editor can handle 3 dimention arrays of editor can handle 3 dimention arrays of neurons as single entities with very flexible neurons as single entities with very flexible interconect patterns. One can then design a interconect patterns. One can then design a scenario with walls, rocks, lights, fat (fuel) scenario with walls, rocks, lights, fat (fuel) sources (that can be smelled) and many sources (that can be smelled) and many other such things. Robot tanks are then other such things. Robot tanks are then introduced into the Scenario and allowed introduced into the Scenario and allowed interact or battle it out. The last one alive interact or battle it out. The last one alive wins, or maybe one just watches the motion wins, or maybe one just watches the motion of the robots for fun. While the scenario is of the robots for fun. While the scenario is running it can be stopped, edited, zoom'd, running it can be stopped, edited, zoom'd, and can track on any robot. and can track on any robot.


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


Eventualy I expect to do a port to UNIX Eventualy I expect to do a port to UNIX and multi thread the sign. I also expect and multi thread the sign. I also expect to do a Mac port and maybe NT or OS/2 to do a Mac port and maybe NT or OS/2 Copies of NuTank cost $50 each. Copies of NuTank cost $50 each. Contact: Richard Keene; Keene Contact: Richard Keene; Keene Educational Software; Educational Software; [email protected] NuTank [email protected] NuTank shareware with the Save options shareware with the Save options disabled is available via anonymous ftp disabled is available via anonymous ftp from the Internet, see the file from the Internet, see the file /pub/incoming/nutank.readme on the /pub/incoming/nutank.readme on the host cher.media.mit.edu.host cher.media.mit.edu.


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


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


12. NeuFuz4 +++++++++++ Name: 12. NeuFuz4 +++++++++++ Name: NeuFuz4 Company: National NeuFuz4 Company: National Semiconductor Corporation Address: Semiconductor Corporation Address: 2900 Semiconductor Drive, Santa 2900 Semiconductor Drive, Santa Clara, CA, 95052, or: Industriestrasse Clara, CA, 95052, or: Industriestrasse 10, D-8080 Fuerstenfeldbruck, 10, D-8080 Fuerstenfeldbruck, Germany, or: Sumitomo Chemical Germany, or: Sumitomo Chemical Engineering Center, Bldg. 7F 1-7-1, Engineering Center, Bldg. 7F 1-7-1, Nakase, Mihama-Ku, Chiba-City, Ciba Nakase, Mihama-Ku, Chiba-City, Ciba Prefecture 261, JAPAN, or: 15th Floor, Prefecture 261, JAPAN, or: 15th Floor, Straight Block, Ocean Centre, 5 Straight 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 Germany : 0l1-81-3-3299-7001 Japan : (852) 737-1600 Hong Kong Japan : (852) 737-1600 Hong Kong Email: [email protected] (Neural Email: [email protected] (Neural net inquiries only) URL: net inquiries only) URL: http://www.commerce.net/directorihttp://www.commerce.net/directories/participants/ns/home.html Basic es/participants/ns/home.html Basic capabilities: Uses backpropagation capabilities: Uses backpropagation techniques to initially select fuzzy techniques to initially select fuzzy rules and membership functions. rules and membership functions.


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


The systems are aimed at low end The 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 technology NeuFuz is a neural-fuzzy technology which uses backpropagation which uses backpropagation techniques to initially select fuzzy techniques to initially select fuzzy rules and membership functions. rules and membership functions. Initial stages of design using NeuFuz Initial stages of design using NeuFuz technology are performed using technology are performed using training data and backpropagation. training data and backpropagation.


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


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


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


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


contingency table analysis / contingency table analysis / correspondence analysis - univariate correspondence analysis - univariate histogram analysis - extensive set of histogram analysis - extensive set of smoothing and normalization smoothing and normalization transformations - easily and quickly transformations - easily and quickly plot color-coded 1-D curves and plot color-coded 1-D curves and histograms, 2-D, 3-D, and N-D histograms, 2-D, 3-D, and N-D mapped scatterplots, highlighting mapped scatterplots, highlighting selected patterns - Command Line selected patterns - Command Line (Tcl) and Graphical Interface * Pattern (Tcl) and Graphical Interface * Pattern Visualization System (TM) The Pattern Visualization System (TM) The Pattern Visualization System offers the most Visualization System offers the most powerful tools for visual analysis of powerful tools for visual analysis of the patterns in your data. Some the 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 your D for visualization of *all* of your variables at once - hard copy color variables at once - hard copy color Postscript output - a variety of color-Postscript output - a variety of color-coding, highlighting, and labeling coding, highlighting, and labeling options allow you to generate options allow you to generate meaningful graphics * Data Filters meaningful graphics * Data Filters Filter out selected rows and/or columns Filter out selected rows and/or columns of your data for flexible and efficient of your data for flexible and efficient cross-validation, jackknifing, cross-validation, jackknifing, bootstrapping, feature set evaluation, bootstrapping, feature set evaluation, and more. * Random # Generators and more. * Random # Generators Generate random numbers from any of Generate random numbers from any of the following parameterized the 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 distance Choose the appropriate distance metric for your data: - euclidean, metric for your data: - euclidean, mahalanobis, minkowski, maximum mahalanobis, minkowski, maximum value, absolute value, shape value, absolute value, shape coefficient, cosine coefficient, pearson coefficient, cosine coefficient, pearson correlation, rank correlation, kendall's correlation, rank correlation, kendall's tau, canberra, and bray-curtis * Tcl/Tk tau, canberra, and bray-curtis * Tcl/Tk command line interface 2. The command line interface 2. The PARTEK/DSA - Data Structure Analysis PARTEK/DSA - Data Structure Analysis Module * Principal Components Module * Principal Components Analysis and Regression Also known as Analysis and Regression Also known as Eigenvector Projection or Karhunen-Eigenvector Projection or Karhunen-Loeve Expansions, PCA removes Loeve Expansions, PCA removes redundant information from your data. redundant information from your data.


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


cluster labeled data to a matrix viewer or cluster labeled data to a matrix viewer or the Analytical Spreadsheet for further the Analytical Spreadsheet for further analysis - visualize n-dimensional analysis - visualize n-dimensional clustering - assess goodness of partion clustering - assess goodness of partion using several internal and external using several internal and external criteria metrics * N-Dimensional criteria metrics * N-Dimensional Histogram Analysis Among the most Histogram Analysis Among the most inportant questions a researcher needs inportant questions a researcher needs to know when analyzing patterns is to know when analyzing patterns is whether or not the patterns can whether or not the patterns can distinguish different classes of data. N-D distinguish different classes of data. N-D Histogram Analysis is one tool to answer Histogram Analysis is one tool to answer this question. - measures histogram this question. - measures histogram overlap in n-dimensional space - overlap in n-dimensional space - automatically find the best subset of automatically find the best subset of features - rank the overlap of your best features - rank the overlap of your best feature combinations *feature combinations *


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


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


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


LVQ1, LVQ2, and LVQ3 training methods - 3 LVQ1, LVQ2, and LVQ3 training methods - 3 different functions for adapting learning different functions for adapting learning rate - choose between several distance rate - choose between several distance metrics - fuzzy and crisp classifications - metrics - fuzzy and crisp classifications - set number of prototypes individually for set number of prototypes individually for each class * Bayesian Classifier Bayes each class * Bayesian Classifier Bayes methods are the statistical decision methods are the statistical decision theory approach to classification. This theory approach to classification. This classifier uses statistical properties of classifier uses statistical properties of your data to develop a classification your data to develop a classification model. PARTEK is available on HP, IBM, model. PARTEK is available on HP, IBM, Silicon Graphics, and SUN workstations. Silicon Graphics, and SUN workstations. For more information, send email to For more information, send email to "[email protected]" or call (314)926-2329."[email protected]" or call (314)926-2329.


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