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Fusion of Neural Networks, Fuzzy Systems and Genetic Algorithms: Industrial Applications
25.01.2010, 22:00
 
by Lakhmi C. Jain; N.M. Martin CRC Press, CRC Press LLC
ISBN: 0849398045 Pub Date: 11/01/98
Chapter 1—Introduction to Neural Networks, Fuzzy Systems, Genetic Algorithms, and their Fusion
1. Knowledge-Based Information Systems
2. Artificial Neural Networks
3. Evolutionary Computing
4. Fuzzy Logic
5. Fusion
6. Summary References
 
Chapter 2—A New Fuzzy-Neural Controller
1. Introduction
2. RBF Based Fuzzy System with Unsupervised Learning
2.1 Fuzzy System Based on RBF
2.2 Coding
2.3 Selection
2.4 Crossover Operator
2.5 Mutation Operator
3. Hierarchical Fuzzy-Neuro Controller Based on Skill Knowledge Database
4. Fuzzy-Neuro Controller for Cart-Pole System 5. Conclusions References
 
Chapter 3—Expert Knowledge-Based Direct Frequency Converter Using Fuzzy Logic Control
1. Introduction
2. XDFC Topology and Operation
3. Space Vector Model of the DFC
4. Expert Knowledge-Based SVM
5. XDFC Control
5.1 XDFC Control Strategy and Operation
5.2 Fuzzy Logic Controller
5.3 Load’s Line Current Control
5.4 Input’s Line Current Control
6. Results
7. Evaluation
8. Conclusion References
 
Chapter 4—Design of an Electro-Hydraulic System Using Neuro-Fuzzy
Techniques 1.
Introduction
2. The Fuzzy Logic System
2.1 Fuzzification
2.2 Inference Mechanism
2.3 Defuzzification
3. Fuzzy Modeling
4. The Learning Mechanism
4.1 Model Initialization
4.2 The Cluster-Based Algorithm
4.3 Illustrative Example
4.4 The Neuro-Fuzzy Algorithm
5. The Experimental System
5.1 Training Data Generation
6. Neuro-Fuzzy Modeling of the Electro-Hydraulic Actuator
7. The Neuro-Fuzzy Control System
7.1 Experimental Results
8. Conclusion References
 
Chapter 5—Neural Fuzzy Based Intelligent Systems and Applications
1. Introduction
2. Advantages and Disadvantages of Fuzzy Logic and Neural Nets
2.1 Advantages of Fuzzy Logic
2.2 Disadvantages of Fuzzy Logic
2.3 Advantages of Neural Nets
 
Категория: Нечеткие системы и нечеткая логика | Добавил: Andreeva
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