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Principles of artificial neural networks /

The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural networ...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Graupe, Daniel
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Singapore ; Hackensack, N.J. : World Scientific, ©2007.
Edición:2nd ed.
Colección:Advanced series on circuits and systems ; v. 6.
Temas:
Acceso en línea:Texto completo

MARC

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245 1 0 |a Principles of artificial neural networks /  |c Daniel Graupe. 
250 |a 2nd ed. 
260 |a Singapore ;  |a Hackensack, N.J. :  |b World Scientific,  |c ©2007. 
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490 1 |a Advanced series on circuits and systems ;  |v vol. 6 
504 |a Includes bibliographical references (pages 291-297) and indexes. 
588 0 |a Print version record. 
505 0 |a Introduction and role of artificial neural networks -- Fundamentals of biological neural networks -- Basic principles of ANNs and their early structures -- The Perceptron -- The Madaline -- Back propagation -- Hopfield networks -- Counter propagation -- Adaptive resonance theory -- The Cognitron and the Neocognitron -- Statistical training -- Recurrent (Time cycling) back propagation networks -- Large scale memory storage and retrieval (LAMSTAR) network. 
520 |a The book should serve as a text for a university graduate course or for an advanced undergraduate course on neural networks in engineering and computer science departments. It should also serve as a self-study course for engineers and computer scientists in the industry. Covering major neural network approaches and architectures with the theories, this text presents detailed case studies for each of the approaches, accompanied with complete computer codes and the corresponding computed results. The case studies are designed to allow easy comparison of network performance to illustrate strength. 
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650 0 |a Neural networks (Computer science) 
650 6 |a Réseaux neuronaux (Informatique) 
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830 0 |a Advanced series on circuits and systems ;  |v v. 6. 
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