فنون و کاربردهای سیستم‌های شبکه عصبی، کاربردهای منطق فازی و سیستم‌های خبره، جلد ششم

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. 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 Front Cover......Page 1 Fuzzy Logic and Expert Systems Applications......Page 4 Copyright Page......Page 5 Contents......Page 6 Contributors......Page 14 Preface......Page 16 I. Introduction......Page 22 II. Fuzzy Classification and Fuzzy Modeling by Nonfuzzy Neural Networks......Page 27 III. Interval-Arithmetic-Based Neural Networks......Page 48 IV. Fuzzified Neural Networks......Page 61 V. Conclusion......Page 72 References......Page 73 I. Introduction......Page 78 II. Structure of Fuzzy Systems for Modeling and Control......Page 81 III. Design 1: A Fuzzy Neural Network with an Additional OR Layer......Page 97 IV. Design 2: A Fuzzy Neural Network Based on Hierarchical Space Partitioning......Page 115 V. Conclusion......Page 138 Appendix......Page 139 References......Page 141 I. Introduction......Page 144 II. Nonlinear Thresholded Artificial Neurons......Page 145 III. Production Rules......Page 146 IV. Forward Chaining......Page 148 V. Chunking......Page 153 VI. Neural Tools for Uncertain Reasoning: Toward Hybrid Extensions......Page 161 VII. Qualitative and Quantitative Uncertain Reasoning......Page 166 VIII. Purely Neural, Rule-Based Diagnostic System......Page 179 IX. Conclusions......Page 192 References......Page 194 I. Introduction......Page 196 II. Representation of a Neuron......Page 197 III. Converting Neural Networks to Boolean Functions......Page 200 IV. Example Application of Boolean Rule Extraction......Page 206 V. Network Design, Pruning, and Weight Decay......Page 208 VI. Simplifying the Derived Rule Base......Page 213 VII. Example of the Construction of a Rule-Based Intelligent System......Page 218 VIII. Using Rule Extraction to Verify the Networks......Page 223 IX. Conclusions......Page 229 References......Page 230 I. Introduction......Page 232 II. Expert Systems: Some Problems and Relevance of Soft Computing......Page 235 III. Connectionist Expert Systems: A Review......Page 246 IV. Neuro-Fuzzy Expert Systems......Page 248 V. Other Hybrid Models......Page 255 References......Page 258 I. Introduction......Page 264 II. Fuzzy Reasoning Schemes......Page 266 III. Design of the Conclusion Part in Functional Reasoning......Page 269 IV. Fuzzy Gaussian Neural Networks......Page 270 V. Attitude Control Application Example......Page 279 VI. Mobile Robot Example......Page 291 VII. Conclusions......Page 302 References......Page 303 I. Introduction......Page 306 II. Fuzzy Neural Network......Page 307 III. Mapping Capability of the Fuzzy Neural Network......Page 320 IV. Model Reference Control System Using a Fuzzy Neural Network......Page 326 V. Simulation Results......Page 330 References......Page 333 I. Introduction, Motivations, Basic Problems......Page 336 II "Classical" Methods of Nonlinear System Identification......Page 348 III. Wavelets: What They Are, and Their Use in Approximating Functions......Page 365 IV. Wavelets: Their Use in Nonparametric Estimation......Page 377 V. Wavelet Network for Practical System Identification......Page 384 VI. Fuzzy Models: Expressing Prior Knowledge in Nonlinear Nonparametric Models......Page 391 VII. Experimental Results......Page 400 VIII. Discussion and Conclusions......Page 418 IX. Appendix: Three Methods for Regressor Selection......Page 421 References......Page 430 Index......Page 434 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