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.

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