Neural Networks: A Comprehensive Foundation (2nd Edition)

From Book News, Inc.
A textbook for a graduate course in engineering, computer science, and physics, but also perhaps useful for researchers in psychology and the neurosciences. Covers the nature of neural networks in largely qualitative terms, learning machines with and without a teacher, and nonlinear dynamical systems. The text is supported with examples, computer-oriented experiments, end-of- chapter problems, and two web sites. An instructor's manual is available. No date is mentioned for the first edition. Book News, Inc.®, Portland, OR

Book Info
Provides a comprehensive foundation of neural networks, recognizing the multidisciplinary nature of the subject, supported with examples, computer-oriented experiments, end of chapter problems, and a bibliography. DLC: Neural networks (Computer science).

The publisher, Prentice-Hall Engineering/Science/Mathematics
This text represents the first comprehensive treatment of neural networks from an engineering perspective. Thorough, well-organized, and completely up-to-date, it examines all the important aspects of this emerging technology. Neural Networks provides broad coverage of the subject, including the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementations. Chapter objectives, computer experiments, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary reinforce key concepts. The author's concise and fluid writing style makes the material more accessible.

From the Back Cover
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 NEW—New chapters now cover such areas as: Support vector machines. Reinforcement learning/neurodynamic programming. Dynamically driven recurrent networks. NEW-End—of-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 process—an 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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A textbook for a graduate course in engineering, computer science, and physics, but also perhaps useful for researchers in psychology and the neurosciences. Covers the nature of neural networks in largely qualitative terms, learning machines with and without a teacher, and nonlinear dynamical systems. The text is supported with examples, computer-oriented experiments, end-of- chapter problems, and two web sites. An instructor's manual is available. No date is mentioned for the first edition. Annotation c. by Book News, Inc., Portland, Or.


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