Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms

Overview

Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume.
This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks...

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Paperback (Softcover reprint of the original 1st ed. 1997)
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Overview

Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume.
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.

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Editorial Reviews

From the Publisher
'Overall Intelligent Hybrid Systems is an extemely useful book. It is absolutely essential for anyone attempting to keep with recent developments in soft computing, and in some contexts it may serve as an introductory guide.'
International Journal General Systems, 29:2
Booknews
Twenty-nine global contributors attest to the facts that the methodologies of fuzzy logic, neural networks, and genetic algorithms are revolutionary, biologically inspired, capable of solving nonlinear problems, and most powerful as approximate-reasoning frameworks when integrated into hybrid models. Basic principles and methodologies are overviewed for system engineering researchers as well as cognitive scientists, as are the data analysis and information systems pivotal to intelligent system applications, and the genetic programming and design challenges of complex nonlinear systems and system identification. Annotation c. by Book News, Inc., Portland, Or.
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Product Details

  • ISBN-13: 9781461378389
  • Publisher: Springer US
  • Publication date: 12/31/2012
  • Edition description: Softcover reprint of the original 1st ed. 1997
  • Edition number: 1
  • Pages: 354
  • Product dimensions: 6.14 (w) x 9.21 (h) x 0.78 (d)

Table of Contents

Foreword; P.P. Wang. Editor's Preface; Da Ruan. Part 1: Basic Principles and Methodologies. 1. Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms; H. Takagi. 2. A Fuzzy Neural Network for Approximate Fuzzy Reasoning; L.P. Maguire, et al. 3. Novel Neural Algorithms for Solving Fuzzy Relation Equations; Xiaozhong Li, Da Ruan. 4. Methods for Simplification of Fuzzy Models; U. Kaymak, et al. 5. A New Approach of Neurofuzzy Learning Algorithm; M. Mizumoto, Yan Shi. Part 2: Data Analysis and Information Systems. 6. Neural Networks in Intelligent Data Analysis; Xiaohui Liu. 7. Data-Driven Identification of Key Variables; Bo Yuan, G. Klir. 8. Applications of Intelligent Techniques in Process Analysis; J. Angstenberger, R. Weber. 9. Neurofuzzy-Chaos Engineering for Building Intelligent Adaptive Information Systems; N.K. Kasabov, R. Kozma. 10. A Sequential Training Strategy for Locally Recurrent Neural Networks; Jie Zhang, A.J. Morris. Part 3: Nonlinear Systems and System Identification. 11. Adaptive Genetic Programming for System Identification; A. Bastian. 12. Nonlinear System Identification with Neurofuzzy Methods; O. Nelles. 13. A Genetic Algorithm for Mixed-Integer Optimisation in Power and Water System Design and Control; Kai Chen, et al. 14. Soft Computing Based Signal Prediction, Restoration, and Filtering; E. Uchino, T. Yamakawa. Subject Index.

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