Fluid and authoritative, this well-organized book represents the first comprehensive treatment of neural networks and learning machines from an engineering perspective, providing extensive, state-of-the-art coverage that will expose readers to the myriad facets of neural networks and help them appreciate the technology's origin, capabilities, and potential applications. Examines all the important aspects of this emerging technology, covering the learning process, back propogation, radial basis functions, recurrent networks, self-organizing systems, modular networks, temporal processing, neurodynamics, and VLSI implementation. Integrates computer experiments throughout to demonstrate how neural networks are designed and perform in practice. Chapter objectives, problems, worked examples, a bibliography, photographs, illustrations, and a thorough glossary all reinforce concepts throughout. New chapters delve into such areas as support vector machines, and reinforcement learning/neurodynamic programming,  Rosenblatt’s Perceptron, Least-Mean-Square Algorithm, Regularization Theory, Kernel Methods and Radial-Basis function networks (RBF), and Bayseian Filtering for State Estimation of Dynamic Systems. An entire chapter of case studies illustrates the real-life, practical applications of neural networks. A highly detailed bibliography is included for easy reference. For professional engineers and research scientists.

Matlab codes used for the computer experiments in the text are available for download at: http://www.pearsonhighered.com/haykin/