Neural Networks: A Comprehensive Foundation

Haykin, Simon

ISBN 10: 0132733501 / ISBN 13: 9780132733502

Published by Prentice Hall, 1998

About this Item

LIGHTNING FAST SHIPPING! A heavily used, but still working copy. Coffee stain and wrinkling to the edge of the pages when a tired undergrad fell asleep and knocked their cup over on their books. The binding and pages of the book have been reinforced with tape, has tape and stickers on the cover, as well as lots of notes (some of the answers in the learning activities may be filled in) on the pages. Definitely not pretty, but it's a working copy at a great price that ships fast. ~ Book does NOT contain an access code or CD/DVD.

Seller Inventory # #193C-00109

Bibliographic Details

• Title: Neural Networks: A Comprehensive Foundation
• Publisher: Prentice Hall
• Publication Date: 1998
• Language: English
• ISBN 10: 0132733501
• ISBN 13: 9780132733502
• Binding: Hardcover
• Condition: Acceptable

About this title

Synopsis

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.

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.