Simon Haykin "Neural Networks A comprehensive Foundation" 2 ed.

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Simon Haykin, "Neural Networks. A comprehensive Foundation", 2 ed.
Publisher: Prentice Hal | 2005 | ISBN: 8178083000 | English | PDF | 823 pages | 39.85 Mb

"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..."




"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.

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.
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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Tags: Foundation, Comprehensive, Haykin, Neural, Networks
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