Neural Networks: A Comprehensive Foundation (2nd Edition)

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9780132733502: Neural Networks: A Comprehensive Foundation (2nd Edition)
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For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science.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.

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From the Publisher:

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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