Intelligent Hybrid Systems Fuzzy Logic, Neural Networks, and Genetic Algorithms
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 l...
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| Format: | eBook |
| Language: | English |
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New York, NY
Springer US
1997, 1997
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| Edition: | 1st ed. 1997 |
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| Online Access: | |
| Collection: | Springer Book Archives -2004 - Collection details see MPG.ReNa |
Table of Contents:
- 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