Overview
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Table of contents (14 chapters)
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Front Matter
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Basic Principles and Methodologies
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Front Matter
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Data Analysis and Information Systems
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Front Matter
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Nonlinear Systems and System Identification
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Front Matter
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Back Matter
Keywords
About this book
This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks and evolutionary computation. The emphasis is on development of cooperative models of hybrid systems. Included are applications related to intelligent data analysis, process analysis, intelligent adaptive information systems, systems identification, nonlinear systems, power and water system design, and many others.
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms provides researchers and engineers with up-to-date coverage of new results, methodologies and applications for building intelligent systems capable of solving large-scale problems.
Reviews
International Journal General Systems, 29:2
Editors and Affiliations
Bibliographic Information
Book Title: Intelligent Hybrid Systems
Book Subtitle: Fuzzy Logic, Neural Networks, and Genetic Algorithms
Editors: Da Ruan
DOI: https://doi.org/10.1007/978-1-4615-6191-0
Publisher: Springer New York, NY
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eBook Packages: Springer Book Archive
Copyright Information: Springer Science+Business Media New York 1997
Hardcover ISBN: 978-0-7923-9999-5Published: 30 September 1997
Softcover ISBN: 978-1-4613-7838-9Published: 02 October 2012
eBook ISBN: 978-1-4615-6191-0Published: 06 December 2012
Edition Number: 1
Number of Pages: XIX, 354
Topics: Mathematical Logic and Foundations, Complex Systems, Artificial Intelligence, Statistical Physics and Dynamical Systems