Hardcover. This excellent reference for all those involved in neural networks research and application presents, in a single text, the necessary aspects of parallel implementation for all major artificial neural network models. The book details implementations on varoius processor architectures (ring, torus, etc.) built on different hardware platforms, ranging from large general purpose parallel computers to custom built MIMD machines using transputers and DSPs. Experts who performed the implementations author the chapters and research results are covered in each chapter. These results are divided into three parts. Theoretical analysis of parallel implementation schemes on MIMD message passing machines. Details of parallel implementation of BP neural networks on a general purpose, large, parallel computer. Four chapters each describing a specific purpose parallel neural computer configuration. This book is aimed at graduate students and researchers working in artificial neural networks and parallel computing. Graduate level educators can use it to illustrate the methods of parallel computing for ANN simulation. The text is an ideal reference providing lucid mathematical analyses for practitioners in the field. Presents the parallel implementation aspects of all major artificial network models. The text details implementations on various processor architectures built on different hardware platforms, ranging from large parallel computers to MIMD machines using transputers and DSPs. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

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Detalles bibliogr�ficos

T�tulo

Parallel Architectures for Artificial Neural Networks (Hardcover)

Editor

IEEE Computer Society Press,U.S., Los Alamitos, CA

Fecha de publicaci�n

1998

Idioma

Ingl�s

ISBN 10

0818683996

ISBN 13

9780818683992

Encuadernaci�n

Hardcover

Edici�n

1� Edici�n

Estado

new

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Sinopsis

This excellent reference for all those involved in neural networks research and application presents, in a single text, the necessary aspects of parallel implementation for all major artificial neural network models. The book details implementations on varoius processor architectures (ring, torus, etc.) built on different hardware platforms, ranging from large general purpose parallel computers to custom built MIMD machines using transputers and DSPs.

Experts who performed the implementations author the chapters and research results are covered in each chapter. These results are divided into three parts.

Theoretical analysis of parallel implementation schemes on MIMD message passing machines.

Details of parallel implementation of BP neural networks on a general purpose, large, parallel computer.

Four chapters each describing a specific purpose parallel neural computer configuration.

This book is aimed at graduate students and researchers working in artificial neural networks and parallel computing. Graduate level educators can use it to illustrate the methods of parallel computing for ANN simulation. The text is an ideal reference providing lucid mathematical analyses for practitioners in the field.

Acerca del autor

N. Sundararajan and P. Saratchandran are the authors of Parallel Architectures for Artificial Neural Networks: Paradigms and Implementations, published by Wiley.

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