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Neural Networks : A Comprehensive Foundation / Edition 2

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Overview

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.

This book presents 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.

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Editorial Reviews

Booknews
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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Product Details

  • ISBN-13: 9780132733502
  • Publisher: Prentice Hall
  • Publication date: 7/6/1998
  • Edition description: REV
  • Edition number: 2
  • Pages: 842
  • Product dimensions: 6.92 (w) x 9.36 (h) x 1.61 (d)

Table of Contents

Preface
Acknowledgments
Abbreviations and Symbols
1 Introduction 1
2 Learning Processes 50
3 Single Layer Perceptrons 117
4 Multilayer Perceptrons 156
5 Radial-Basis Function Networks 256
6 Support Vector Machines 318
7 Committee Machines 351
8 Principal Components Analysis 392
9 Self-Organizing Maps 443
10 Information-Theoretic Models 484
11 Stochastic Machines And Their Approximates Rooted in Statistical Mechanics 545
12 Neurodynamic Programming 603
13 Temporal Processing Using Feedforward Networks 635
14 Neurodynamics 664
15 Dynamically Driven Recurrent Networks 732
Epilogue 790
Bibliography 796
Index 837
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  • Anonymous

    Posted March 23, 2007

    Neural and Design

    If you are in a process of designing a system based on artificial neural theory, consider this book extremely useful. Dr. Syed Rashdee Professor - Senior Project Advisor

    Was this review helpful? Yes  No   Report this review
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