Ebook: Advanced Fuzzy Systems Design and Applications

Fuzzy rule systems have found a wide range of applications in many fields of science and technology. Traditionally, fuzzy rules are generated from human expert knowledge or human heuristics for relatively simple systems. In the last few years, data-driven fuzzy rule generation has been very active. Compared to heuristic fuzzy rules, fuzzy rules generated from data are able to extract more profound knowledge for more complex systems. This book presents a number of approaches to the generation of fuzzy rules from data, ranging from the direct fuzzy inference based to neural net­ works and evolutionary algorithms based fuzzy rule generation. Besides the approximation accuracy, special attention has been paid to the interpretabil­ ity of the extracted fuzzy rules. In other words, the fuzzy rules generated from data are supposed to be as comprehensible to human beings as those generated from human heuristics. To this end, many aspects of interpretabil­ ity of fuzzy systems have been discussed, which must be taken into account in the data-driven fuzzy rule generation. In this way, fuzzy rules generated from data are intelligible to human users and therefore, knowledge about unknown systems can be extracted.

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This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented.

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This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented.
Content:
Front Matter....Pages I-X
Fuzzy Sets and Fuzzy Systems....Pages 1-47
Evolutionary Algorithms....Pages 49-71
Artificial Neural Networks....Pages 73-91
Conventional Data-driven Fuzzy Systems Design....Pages 93-110
Neural Network Based Fuzzy Systems Design....Pages 111-141
Evolutionary Design of Fuzzy Systems....Pages 143-171
Knowledge Discovery by Extracting Interpretable Fuzzy Rules....Pages 173-204
Fuzzy Knowledge Incorporation into Neural Networks....Pages 205-221
Fuzzy Preferences Incorporation into Multi-objective Optimization....Pages 223-253
Back Matter....Pages 255-272

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This book presents a variety of recently developed methods for generating fuzzy rules from data with the help of neural networks and evolutionary algorithms. Special efforts have been put on dealing with knowledge incorporation into neural and evolutionary systems and knowledge extraction from data with the help of fuzzy logic. On the one hand, knowledge that is understandable to human beings can be extracted from data using evolutionary and learning methods by maintaining the interpretability of the generated fuzzy rules. On the other hand, a priori knowledge like expert knowledge and human preferences can be incorporated into evolution and learning, taking advantage of the knowledge representation capability of fuzzy rule systems and fuzzy preference models. Several engineering application examples in the fields of intelligent vehicle systems, process modeling and control and robotics are presented.
Content:
Front Matter....Pages I-X
Fuzzy Sets and Fuzzy Systems....Pages 1-47
Evolutionary Algorithms....Pages 49-71
Artificial Neural Networks....Pages 73-91
Conventional Data-driven Fuzzy Systems Design....Pages 93-110
Neural Network Based Fuzzy Systems Design....Pages 111-141
Evolutionary Design of Fuzzy Systems....Pages 143-171
Knowledge Discovery by Extracting Interpretable Fuzzy Rules....Pages 173-204
Fuzzy Knowledge Incorporation into Neural Networks....Pages 205-221
Fuzzy Preferences Incorporation into Multi-objective Optimization....Pages 223-253
Back Matter....Pages 255-272
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