- Provides in-depth treatment of theoretical contributions to optimal learning for neural network systems- Offers a comprehensive treatment of orthogonal transformation techniques for the optimization of neural network systems- Includes illustrative examples and comprehensive treatment of sequential constructive techniques for optimization of neural network systems- Presents a uniquely comprehensive treatment of the highly effective fast back propagation algorithms for the optimization of neural network systems- Treats, in detail, optimization techniques for neural network systems with nonstationary or dynamic inputs- Covers optimization techniques and applications of neural network systems in constraint satisfaction
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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.