©TélécHArGEr. Neural Networks: A Comprehensive Foundation Livre. par Macmillan USA

This book provides a comprehensive foundation of neural networks, recognizing the multidisciplinary nature of the subject. The material presented in the book is supported with examples, computer-oriented experiments, end-of-chapter problems, and a bibliography

This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a

746 Book Reviews which requires an intermediate stage of a parametric Neural Networks: A Comprehensive Foundation, model identification. They show that the by Simon HAYKIN; Macmillan College Publishing~ approach has certain advantages over the direct New York, USA; IEEE Press,New York, USA; control, and present a number of case studies to IEEE Computer SocietyPress,Los Alamitos,CA, illustrate the features of adaptive FLCs when dealing USA; 1994; 696 pp.; $69-95; ISBN: -02-352761-7

Neural networks resemble the brain in two respects: 1. The network acquires knowledge from its environment using a learning process (algorithm) 2. Synaptic weights, which are interneuron connection strenghts, are used to store the learned information. fuLL.y connected 10-4-2 feedforward network with 10 source (input) nodes, 4 hidden neurons, and 2 output neu-