Industrial and Manufacturing Systems serves as an in-depth guide to major applications in this focal area of interest to the engineering community. This volume emphasizes the neural network structures used to achieve practical and effective systems, and provides numerous examples. Industrial and Manufacturing Systems is a unique and comprehensive reference to diverse application methodologies and implementations by means of neural network systems. It willbe of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering.
Emphasis is placed on neural network structures for achieving practical and effective systems, with numerous examples illustrating the text; Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering, will find this volume a unique and comprehensive reference to diverse application methodologies and implementations by means of neura network systems.
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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.
Inspired by the structure of the human brain, artificial neural networks have found many applications due to their ability to solve cumbersome or intractable problems by learning directly from data. Neural networks can adapt to new environments by learning, and deal with information that is noisy, inconsistent, vague, or probabilistic. This volume of Neural Network Systems Techniques and Applications is devoted to Industrial and Manufacturing Systems, and will serve as an in-depth guide to major applications in a focal area of interest to the engineering community.
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