Neural networks. A comprehensive foundation.

Haykin, Simon S.:

ISBN 10: 0139083855 / ISBN 13: 9780139083853

Published by Upper Saddle River, Prentice-Hall, 1999

About this Item

842 S. Ehem. Bibliotheksexemplar mit Signatur und Stempel. GUTER Zustand, ein paar Gebrauchsspuren. Ex-library with stamp and library-signature. GOOD condition, some traces of use. AD6295 9780139083853 Sprache: Englisch Gewicht in Gramm: 1250.

Seller Inventory # 2368606

Bibliographic Details

• Title: Neural networks. A comprehensive foundation.
• Publisher: Upper Saddle River, Prentice-Hall
• Publication Date: 1999
• Language: English
• ISBN 10: 0139083855
• ISBN 13: 9780139083853
• Binding: Softcover
• Edition: 2. ed., [Nachdr.], internat. ed.
• Seller catalogs:
  • Q Wirtschaftswissenschaften

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.