Fluid and authoritative, this well-organized book
represents the first comprehensive treatment of
neural
networks 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 technolgy, 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, plus
readers will find an entire chapter of case studies to
illustrate the real-life, practical applications of neural
networks. A highly detailed bibliography is included for
easy reference. For professional engineers and research
scientists.