Parallel architectures for artificial neural networks
paradigms and implementations
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Author
Contributions
- Sundararajan, N. - Contributor
- Saratchandran, P. - Contributor
Publication
1998 - IEEE Computer Society, Los Alamitos, Calif, California
Language
English
Word Count
94,750 words, Guess
Page Count
379 pages
Identifiers
- Open LibraryOL362725M
- ISBN-100818683996
- OCLC Control Number98022934
- OCLC Control Number39033459
- OCLC Control Numberparallelarchitec0000unse
and 2 more
- Library of Congress Control Number98022934
- Goodreads2289449
Classifications
- DDC006.3/2
- LCCQA76.87 .P368 1998
Description
A reference for neural networks research and application, this book covers the parallel implementation aspects of all major artificial neural network models in a single text. Parallel Architectures for Artificial Neural Networks details implementations on various processor architectures built on different hardware platforms, ranging from large, general purpose parallel computers to custom built MIMD machines. Aimed at graduate students and researchers working in artificial neural networks and parallel computing, this work can be used by graduate level educators to illustrate parallel computing methods for ANN simulation.
Subjects
Other Editions
- Parallel architectures for artificial neural networks: paradigms and implementations
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