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Sinopsis
Artificial neural networks have been widely applied to many fields--such as pattern recognition, optimization, coding, and control--due to their capability of solving cumbersome or intractable problems by learning directly from data. Neural networks adapt to new environments and deal with information that is noisy, inconsistent, vague, or probabilistic. These features have motivated extensive research and developments in artificial neural networks. A unique and comprehensive reference, this Series covers the different techniques, applications, and systems of artificial neural networks. It will be a valuable and powerful resource for a wide array of practitioners, researchers, and students, including those in the fields of industrial, manufacturing, electrical, and mechanical engineering, as well as computer science and engineering.
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Acerca del autor
Cornelius T. Leondes received his B.S., M.S., and Ph.D. from the University of Pennsylvania and has held numerous positions in industrial and academic institutions. He is currently a Professor Emeritus at the University of California, Los Angeles. He has also served as the Boeing Professor at the University of Washington and as an adjunct professor at the University of California, San Diego. He is the author, editor, or co-author of more than 100 textbooks and handbooks and has published more than 200 technical papers. In addition, he has been a Guggenheim Fellow, Fulbright Research Scholar, IEEE Fellow, and a recipient of IEEE's Baker Prize Award and Barry Carlton Award.
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