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＜電子ブック＞
Intelligent Hybrid Systems : Fuzzy Logic, Neural Networks, and Genetic Algorithms / edited by Da Ruan

版	1st ed. 1997.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	1997
本文言語	英語
大きさ	XIX, 354 p : online resource
著者標目	Da Ruan editor SpringerLink (Online service)
件　名	LCSH:Mathematical logic LCSH:System theory LCSH:Artificial intelligence LCSH:Mathematical physics FREE:Mathematical Logic and Foundations FREE:Complex Systems FREE:Artificial Intelligence FREE:Theoretical, Mathematical and Computational Physics
一般注記	1: Basic Principles and Methodologies -- 1 Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms -- 2 A Fuzzy Neural Network for Approximate Fuzzy Reasoning -- 3 Novel Neural Algorithms for Solving Fuzzy Relation Equations -- 4 Methods for Simplification of Fuzzy Models -- 5 A New Approach of Neurofuzzy Learning Algorithm -- 2: Data Analysis and Information Systems -- 6 Neural Networks in Intelligent Data Analysis -- 7 Data-Driven Identification of Key Variables -- 8 Applications of Intelligent Techniques in Process Analysis -- 9 Neurofuzzy-Chaos Engineering for Building Intelligent Adaptive Information Systems -- 10 A Sequential Training Strategy for Locally Recurrent Neural Networks -- 3: Nonlinear Systems and System Identification -- 11 Adaptive Genetic Programming for System Identification -- 12 Nonlinear System Identification with Neurofuzzy Methods -- 13 A Genetic Algorithm for Mixed-Integer Optimisation in Power and Water System Design and Control -- 14 Soft Computing Based Signal Prediction, Restoration, and Filtering 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 HTTP:URL=https://doi.org/10.1007/978-1-4615-6191-0

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