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From the Publisher: 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.