Fuzzy Logic and Expert Systems Applications
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Table of contents
- Cover
- Contentsv
- Contributorsxiii
- Prefacexv
- Chapter 1. Fuzzy Neural Networks Techniques and Their Applications1
- I. Introduction1
- II. Fuzzy Classification and Fuzzy Modeling by Nonfuzzy Neural Networks6
- III. Interval-Arithmetic-Based Neural Networks27
- IV. Fuzzified Neural Networks40
- V. Conclusion51
- References52
- Chapter 2. Implementation of Fuzzy Systems57
- I. Introduction57
- II. Structure of Fuzzy Systems for Modeling and Control60
- III. Design 1: A Fuzzy Neural Network with an Additional OR Layer76
- IV. Design 2: A Fuzzy Neural Network Based on Hierarchical Space Partitioning94
- V. Conclusion117
- Appendix118
- References120
- Chapter 3. Neural Networks and Rule-Based Systems123
- I. Introduction123
- II. Nonlinear Thresholded Artificial Neurons124
- III. Production Rules125
- IV. Forward Chaining127
- V. Chunking132
- VI. Neural Tools for Uncertain Reasoning: Toward Hybrid Extensions140
- VII. Qualitative and Quantitative Uncertain Reasoning145
- VIII. Purely Neural, Rule-Based Diagnostic System158
- IX. Conclusions171
- References173
- Chapter 4. Construction of Rule-Based Intelligent Systems175
- I. Introduction175
- II. Representation of a Neuron176
- III. Converting Neural Networks to Boolean Functions179
- IV. Example Application of Boolean Rule Extraction185
- V. Network Design, Pruning, and Weight Decay187
- VI. Simplifying the Derived Rule Base192
- VII. Example of the Construction of a Rule-Based Intelligent System197
- VIII. Using Rule Extraction to Verify the Networks202
- IX. Conclusions208
- References209
- Chapter 5. Expert Systems in Soft Computing Paradigm211
- I. Introduction211
- II. Expert Systems: Some Problems and Relevance of Soft Computing214
- III. Connectionist Expert Systems: A Review225
- IV. Neuro-Fuzzy Expert Systems227
- V. Other Hybrid Models234
- VI. Conclusions237
- References237
- Chapter 6. Mean-Value-Based Functional Reasoning Techniques in the Development of Fuzzy-Neural Netwo243
- I. Introduction243
- II. Fuzzy Reasoning Schemes245
- III. Design of the Conclusion Part in Functional Reasoning248
- IV. Fuzzy Gaussian Neural Networks249
- V. Attitude Control Application Example258
- VI. Mobile Robot Example270
- VII. Conclusions281
- References282
- Chapter 7. Fuzzy Neural Network Systems in Model Reference Control Systems285
- I. Introduction285
- II. Fuzzy Neural Network286
- III. Mapping Capability of the Fuzzy Neural Network299
- IV. Model Reference Control System Using a Fuzzy Neural Network305
- V. Simulation Results309
- VI. Conclusions312
- References312
- Chapter 8. Wavelets in Identification315
- I. Introduction, Motivations, Basic Problems315
- II "Classical" Methods of Nonlinear System Identification327
- III. Wavelets: What They Are, and Their Use in Approximating Functions344
- IV. Wavelets: Their Use in Nonparametric Estimation356
- V. Wavelet Network for Practical System Identification363
- VI. Fuzzy Models: Expressing Prior Knowledge in Nonlinear Nonparametric Models370
- VII. Experimental Results379
- VIII. Discussion and Conclusions397
- IX. Appendix: Three Methods for Regressor Selection400
- References409
- Index413
This volume covers the integration of fuzzy logic and expert systems. A vital resource in the field, it includes techniques for applying fuzzy systems to neural networks for modeling and control, systematic design procedures for realizing fuzzy neural systems, techniques for the design of rule-based expert systems using the massively parallel processing capabilities of neural networks, the transformation of neural systems into rule-based expert systems, the characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets, and applications to system identification and control as well as nonparametric, nonlinear estimation. Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will appreciate this reference source to diverse application methodologies.
Key Features
* Fuzzy system techniques applied to neural networks for modeling and control
* Systematic design procedures for realizing fuzzy neural systems
* Techniques for the design of rule-based expert systems
* Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets
* System identification and control
* Nonparametric, nonlinear estimation
Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies
Key Features
* Fuzzy system techniques applied to neural networks for modeling and control
* Systematic design procedures for realizing fuzzy neural systems
* Techniques for the design of rule-based expert systems
* Characteristics and relative merits of integrating fuzzy sets, neural networks, genetic algorithms, and rough sets
* System identification and control
* Nonparametric, nonlinear estimation
Practitioners, researchers, and students in industrial, manufacturing, electrical, and mechanical engineering, as well as computer scientists and engineers will find this volume a unique and comprehensive reference to these diverse application methodologies