Neural Networks (inbunden)
Format
Inbunden (Hardback)
Språk
Engelska
Antal sidor
842
Utgivningsdatum
1998-07-01
Upplaga
2
Förlag
Pearson
Illustrationer
Illustrations
Dimensioner
245 x 185 x 40 mm
Vikt
1300 g
Antal komponenter
1
Komponenter
International edition available ISBN 0139083855
ISBN
9780132733502

Neural Networks

A Comprehensive Foundation: United States Edition

Inbunden, Engelska, 1998-07-01
1148 kr
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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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Innehållsförteckning



 1. Introduction.


 2. Learning Processes.


 3. Single-Layer Perceptrons.


 4. Multilayer Perceptrons.


 5. Radial-Basis Function Networks.


 6. Support Vector Machines.


 7. Committee Machines.


 8. Principal Components Analysis.


 9. Self-Organizing Maps.


10. Information-Theoretic Models.


11. Stochastic Machines & Their Approximates Rooted in Statistical Mechanics.


12. Neurodynamic Programming.


13. Temporal Processing Using Feedforward Networks.


14. Neurodynamics.


15. Dynamically Driven Recurrent Networks.


Epilogue.


Bibliography.


Index.