Parallel implementations of backpropagation neural networks on transputers : a study of training set parallelism
著者
書誌事項
Parallel implementations of backpropagation neural networks on transputers : a study of training set parallelism
（Progress in neural processing, 3）
World Scientific, c1996
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注記
Includes bibliographical references (p. 189-199) and index
内容説明・目次
内容説明
This book presents a systematic approach to parallel implementation of feedforward neural networks on an array of transputers. The emphasis is on backpropagation learning and training set parallelism. Using systematic analysis, a theoretical model has been developed for the parallel implementation. The model is used to find the optimal mapping to minimize the training time for large backpropagation neural networks. The model has been validated experimentally on several well known benchmark problems. Use of genetic algorithms for optimizing the performance of the parallel implementations is described. Guidelines for efficient parallel implementations are highlighted.
目次
- Hardware and software aspects
- transputer topologies for parallel implementation
- comparison between serial and parallel implementation
- analysis and implementation for equal distribution of the training set in a homogeneous transputer array
- analysis and implementation for unequal distribution of the training set in a homogeneous transputer array
- analysis and implementation for unequal distribution of the training set in a heterogeneous transputer array
- conclusion.
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