Neural Networks in a Softcomputing Framework

By: Ke-Lin Du, M.N.S. Swamy

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Hardcover | 18 April 2006

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620 Pages

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23.5 x 16.51 x 3.81

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Neural Networks in a Softcomputing Framework presents a thorough review of the most popular neural-network methods and their associated techniques. This concise but comprehensive textbook provides a powerful and universal paradigm for information processing. Each chapter provides state-of-the-art descriptions of the important major research results of the respective neural-network methods. A range of relevant computational intelligence topics, such as fuzzy logic and evolutionary algorithms - powerful tools for neural-network learning - are introduced. Array signal processing problems are discussed in order to illustrate the applications of each neural-network model. The systematic survey of each neural-network model and the exhaustive list of references will enable researchers and students to find suitable topics for future research. The important algorithms outlined also make this textbook a valuable reference for scientists and practitioners working in pattern recognition, signal processing, speech and image processing, data analysis and artificial intelligence.

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Introduction
Fundamentals of Machine Learning and Softcomputing
Multilayer Perceptrons
Hopfield Networks and Boltzmann Machines
Competitive Learning and Clustering
Radial Basis Function Networks
Principal Component Analysis Networks
Fuzzy Logic and Neuro-fuzzy Systems
Evolutionary Algorithms and Evolving Neural Networks
Discussion and Outlook
Appendix: Mathematical Preliminaries
Table of Contents provided by Publisher. All Rights Reserved.

Neural Networks in a Softcomputing Framework

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Hardcover

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