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Intelligent hybrid systems : fuzzy logic neural networks, and genetic algorithms, Softcover reprint of the original 1st ed. 1997 Fuzzy Logic, Neural Networks, and Genetic Algorithms

Langue : Anglais

Coordonnateur : Da Ruan

Couverture de l’ouvrage Intelligent hybrid systems : fuzzy logic neural networks, and genetic algorithms
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.
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.
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 an 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.

Date de parution :

Ouvrage de 354 p.

15.5x23.5 cm

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Date de parution :

Ouvrage de 376 p.

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