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.