Neural Networks Upplaga 2
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
NEW TO THIS EDITION
NEWNew chapters now cover such areas as: Support vector machines. Reinforcement learning/neurodynamic programming. Dynamically driven recurrent networks. NEW-Endof-chapter problems revised, improved and expanded in number.
FEATURES
Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications. Detailed analysis of back-propagation learning and multi-layer perceptrons. Explores the intricacies of the learning processan essential component for understanding neural networks. Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics. Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice. Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary. Includes a detailed and extensive bibliography for easy reference. Computer-oriented experiments distributed throughout the book Uses Matlab SE version 5.
Upplaga: 2a upplagan
Utgiven: 1998
ISBN: 9780132733502
Förlag: Pearson
Format: Inbunden
Språk: Engelska
Sidor: 842 st
Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Thoroughly revised.
NEW TO THIS EDITION
NEWNew chapters now cover such areas as: Support vector machines. Reinforcement learning/neurodynamic programming. Dynamically driven recurrent networks. NEW-Endof-chapter problems revised, improved and expanded in number.
FEATURES
Extensive, state-of-the-art coverage exposes the reader to the many facets of neural networks and helps them appreciate the technology's capabilities and potential applications. Detailed analysis of back-propagation learning and multi-layer perceptrons. Explores the intricacies of the learning processan essential component for understanding neural networks. Considers recurrent networks, such as Hopfield networks, Boltzmann machines, and meanfield theory machines, as well as modular networks, temporal processing, and neurodynamics. Integrates computer experiments throughout, giving the opportunity to see how neural networks are designed and perform in practice. Reinforces key concepts with chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary. Includes a detailed and extensive bibliography for easy reference. Computer-oriented experiments distributed throughout the book Uses Matlab SE version 5.
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