Parallel Architectures for Artificial Neural Networks: Paradigms and Implementations (Systems)
Sundararajan, N.
ISBN 10: 0818683996 ISBN 13: 9780818683992
Editore: Wiley-IEEE Computer Society Pr, 1998
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Descrizione:
Text block firm and clean, binding unblemished, boards straight, no highlights or underlining. Book only. Well packaged and promptly shipped from California. Partnered with Friends of the Library since 2010. Codice articolo BAY_00_SH_030521
Riassunto:
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.
Informazioni sull'autore:
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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Dati bibliografici
Titolo: Parallel Architectures for Artificial Neural...
Casa editrice: Wiley-IEEE Computer Society Pr
Data di pubblicazione: 1998
Legatura: Rilegato
Condizione: Like New