Angielski
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. 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. It includes topics such as: 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; applications to finite constraint problems; practical realization methods for Hebbian type associative memory systems; parallel self-organizing hierarchical neural network systems; and dynamics of networks of biological neurons for utilization in computational neuroscience. 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 a broad array of algorithms and architectures.
