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1 Simuladores Rna

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1 Simuladores Rna

  1. 1. Enero 2000 ESCOM I P N 1 ** Simuladores de** Simuladores de Redes Neuronales **Redes Neuronales **
  2. 2. Enero 2000 ESCOM I P N 2 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
  3. 3. Enero 2000 ESCOM I P N 3 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
  4. 4. Enero 2000 ESCOM I P N 4 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
  5. 5. Enero 2000 ESCOM I P N 5 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.
  6. 6. Enero 2000 ESCOM I P N 6 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
  7. 7. Enero 2000 ESCOM I P N 7 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.
  8. 8. Enero 2000 ESCOM I P N 8 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.
  9. 9. Enero 2000 ESCOM I P N 9 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
  10. 10. Enero 2000 ESCOM I P N 10 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
  11. 11. Enero 2000 ESCOM I P N 11 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
  12. 12. Enero 2000 ESCOM I P N 12 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.
  13. 13. Enero 2000 ESCOM I P N 13 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.
  14. 14. Enero 2000 ESCOM I P N 14 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.
  15. 15. Enero 2000 ESCOM I P N 15 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
  16. 16. Enero 2000 ESCOM I P N 16 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.
  17. 17. Enero 2000 ESCOM I P N 17 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 .
  18. 18. Enero 2000 ESCOM I P N 18 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
  19. 19. Enero 2000 ESCOM I P N 19 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.
  20. 20. Enero 2000 ESCOM I P N 20 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.
  21. 21. Enero 2000 ESCOM I P N 21 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:
  22. 22. Enero 2000 ESCOM I P N 22 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
  23. 23. Enero 2000 ESCOM I P N 23 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
  24. 24. Enero 2000 ESCOM I P N 24 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.
  25. 25. Enero 2000 ESCOM I P N 25 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
  26. 26. Enero 2000 ESCOM I P N 26 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
  27. 27. Enero 2000 ESCOM I P N 27 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
  28. 28. Enero 2000 ESCOM I P N 28 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.
  29. 29. Enero 2000 ESCOM I P N 29 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
  30. 30. Enero 2000 ESCOM I P N 30 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
  31. 31. Enero 2000 ESCOM I P N 31 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
  32. 32. Enero 2000 ESCOM I P N 32 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)
  33. 33. Enero 2000 ESCOM I P N 33 $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
  34. 34. Enero 2000 ESCOM I P N 34 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
  35. 35. Enero 2000 ESCOM I P N 35 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
  36. 36. Enero 2000 ESCOM I P N 36 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),
  37. 37. Enero 2000 ESCOM I P N 37 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,
  38. 38. Enero 2000 ESCOM I P N 38 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):
  39. 39. Enero 2000 ESCOM I P N 39 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
  40. 40. Enero 2000 ESCOM I P N 40 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...
  41. 41. Enero 2000 ESCOM I P N 41 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
  42. 42. Enero 2000 ESCOM I P N 42 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).
  43. 43. Enero 2000 ESCOM I P N 43 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;
  44. 44. Enero 2000 ESCOM I P N 44 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
  45. 45. Enero 2000 ESCOM I P N 45 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).
  46. 46. Enero 2000 ESCOM I P N 46 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
  47. 47. Enero 2000 ESCOM I P N 47 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)
  48. 48. Enero 2000 ESCOM I P N 48 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
  49. 49. Enero 2000 ESCOM I P N 49 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.
  50. 50. Enero 2000 ESCOM I P N 50 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:
  51. 51. Enero 2000 ESCOM I P N 51 * 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
  52. 52. Enero 2000 ESCOM I P N 52 * 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.
  53. 53. Enero 2000 ESCOM I P N 53 * 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
  54. 54. Enero 2000 ESCOM I P N 54 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
  55. 55. Enero 2000 ESCOM I P N 55 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 -
  56. 56. Enero 2000 ESCOM I P N 56 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
  57. 57. Enero 2000 ESCOM I P N 57 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
  58. 58. Enero 2000 ESCOM I P N 58 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
  59. 59. Enero 2000 ESCOM I P N 59 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
  60. 60. Enero 2000 ESCOM I P N 60 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.
  61. 61. Enero 2000 ESCOM I P N 61 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
  62. 62. Enero 2000 ESCOM I P N 62 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
  63. 63. Enero 2000 ESCOM I P N 63 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.
  64. 64. Enero 2000 ESCOM I P N 64 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.
  65. 65. Enero 2000 ESCOM I P N 65 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.
  66. 66. Enero 2000 ESCOM I P N 66 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.
  67. 67. Enero 2000 ESCOM I P N 67 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
  68. 68. Enero 2000 ESCOM I P N 68 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.
  69. 69. Enero 2000 ESCOM I P N 69 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
  70. 70. Enero 2000 ESCOM I P N 70 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.
  71. 71. Enero 2000 ESCOM I P N 71 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-
  72. 72. Enero 2000 ESCOM I P N 72 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>.
  73. 73. Enero 2000 ESCOM I P N 73 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:
  74. 74. Enero 2000 ESCOM I P N 74 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 -
  75. 75. Enero 2000 ESCOM I P N 75 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: -
  76. 76. Enero 2000 ESCOM I P N 76 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
  77. 77. Enero 2000 ESCOM I P N 77 * 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.
  78. 78. Enero 2000 ESCOM I P N 78 - 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 -
  79. 79. Enero 2000 ESCOM I P N 79 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
  80. 80. Enero 2000 ESCOM I P N 80 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.
  81. 81. Enero 2000 ESCOM I P N 81 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 -
  82. 82. Enero 2000 ESCOM I P N 82 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 -
  83. 83. Enero 2000 ESCOM I P N 83 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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  85. 85. Enero 2000 ESCOM I P N 85 Dudas ???Dudas ???
  86. 86. Enero 2000 ESCOM I P N 86 Hasta la próxima !!!Hasta la próxima !!!

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