Academic Press Algorithms and Architectures (Volume 1) (Neural Network Systems Techniques and Applications, Volume 1)

Product Description This volume is the first diverse and comprehensive treatment of algorithms and architectures for the realization of neural network systems. It presents techniques and diverse methods in numerous areas of this broad subject. The book covers major neural network systems structures for achieving effective systems, and illustrates them with examples.This volume includes Radial Basis Function networks, the Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold Networks, weight initialization, fast and efficient variants of Hamming and Hopfield neural networks, discrete time synchronous multilevel neural systems with reduced VLSI demands, probabilistic design techniques, time-based techniques, techniques for reducing physical realization requirements, and applications to finite constraint problems.A unique and comprehensive reference for a broad array of algorithms and architectures, this book will be of use to practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as in computer science and engineering. Radial Basis Function networksThe Expand-and-Truncate Learning algorithm for the synthesis of Three-Layer Threshold NetworksWeight initializationFast and efficient variants of Hamming and Hopfield neural networksDiscrete time synchronous multilevel neural systems with reduced VLSI demandsProbabilistic design techniquesTime-based techniquesTechniques for reducing physical realization requirementsApplications to finite constraint problemsPractical realization methods for Hebbian type associative memory systemsParallel self-organizing hierarchical neural network systemsDynamics of networks of biological neurons for utilization in computational neuroscience From the Back Cover 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 Algorithms and Architectures for the realization of artificial neural networks. About the Author 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.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.