This book covers the main structure of the neural network system to achieve an effective system, and illustrates them with examples. This volume includes a network of Radial Basis Functions, Expand Learning-and-Cut algorithm for the synthesis of Three-Layer Threshold Networks, weight initialization, fast and efficient variant of Hamming and Hopfield neural network, discrete time synchronous system with the demands of VLSI multilevel neural net, techniques probabilistic design, time-based techniques, techniques to reduce the need for physical realization, and applications to constraint problems is limited. A unique and comprehensive reference for a broad array of algorithms and architecture, this book will be useful to practitioners, researchers, and students in the industrial, manufacturing engineering, electrical, and mechanical, and computer science and engineering. Key Features * Radial Basis Function network * The Expand-and-Cut algorithm for the synthesis of Learning Three-Layer Threshold Networks * Weight initialization * Fast and efficient variant of Hamming and Hopfield neural network * Discrete time multilevel synchronous system with the demands of VLSI neural net * Probabilistic design techniques * Time based techniques * Techniques to reduce the need for physical realization * Applications to the problem constraints limited * Practical realization method for systems Hebbian type associative memory * Parallel self-regulate a system of hierarchical neural network * The dynamics of biological neural networks for use in computational neuroscience Practitioners, researchers, and students in the industrial, manufacturing engineering, electrical, and mechanical, as well as in computer science and engineering, will find this book unique and comprehensive reference to a broad array of algorithms and architecture