Advanced Fuzzy Systems Design and Applications

• Κύριος συγγραφέας: Jin, Yaochu (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Heidelberg : Physica-Verlag HD : Imprint: Physica, 2003.
• Έκδοση: 1st ed. 2003.
• Σειρά: Studies in Fuzziness and Soft Computing, 112
• Θέματα: Computer programming.; Artificial intelligence.; Mathematical logic.; Data structures (Computer science).; Algorithms.; Programming Techniques.; Artificial Intelligence.; Mathematical Logic and Foundations.; Data Structures and Information Theory.; Algorithm Analysis and Problem Complexity.

 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. 

 1. Fuzzy Sets and Fuzzy Systems -- 1.1 Basics of Fuzzy Sets -- 1.2 Fuzzy Rule Systems -- 1.3 Interpretability of Fuzzy Rule System -- 1.4 Knowledge Processing with Fuzzy Logic -- 2. Evolutionary Algorithms -- 2.1 Introduction -- 2.2 Generic Evolutionary Algorithms -- 2.3 Adaptation and Self-Adaptation in Evolutionary Algorithms -- 2.4 Constraints Handling -- 2.5 Multi-objective Evolution -- 2.6 Evolution with Uncertain Fitness Functions -- 2.7 Parallel Implementations -- 2.8 Summary -- 3. Artificial Neural Networks -- 3.1 Introduction -- 3.2 Feedforward Neural Network Models -- 3.3 Learning Algorithms -- 3.4 Improvement of Generalization -- 3.5 Rule Extraction from Neural Networks -- 3.6 Interaction between Evolution and Learning -- 3.7 Summary -- 4. Conventional Data-driven Fuzzy Systems Design -- 4.1 Introduction -- 4.2 Fuzzy Inference Based Method -- 4.3 Wang-Mendel's Method -- 4.4 A Direct Method -- 4.5 An Adaptive Fuzzy Optimal Controller -- 4.6 Summary -- 5.Neural Network Based Fuzzy Systems Design -- 5.1 Neurofuzzy Systems -- 5.2 The Pi-sigma Neurofuzzy Model -- 5.3 Modeling and Control Using the Neurofuzzy System -- 5.4 Neurofuzzy Control of Nonlinear Systems -- 5.5 Summary -- 6. Evolutionary Design of Fuzzy Systems -- 6.1 Introduction -- 6.2 Evolutionary Design of Flexible Structured Fuzzy Controller. -- 6.3 Evolutionary Optimization of Fuzzy Rules -- 6.4 Fuzzy Systems Design for High-Dimensional Systems -- 6.5 Summary -- 7. Knowledge Discovery by Extracting Interpretable Fuzzy Rules -- 7.1 Introduction -- 7.2 Evolutionary Interpretable Fuzzy Rule Generation -- 7.3 Interactive Co-evolution for Fuzzy Rule Extraction -- 7.4 Fuzzy Rule Extraction from RBF Networks -- 7.5 Summary -- 8. Fuzzy Knowledge Incorporation into Neural Networks -- 8.1 Data and A Priori Knowledge -- 8.2 Knowledge Incorporation in Neural Networks for Control -- 8.3 Fuzzy Knowledge Incorporation By Regularization -- 8.4 Fuzzy Knowledge as A Related Task in Learning -- 8.5 Simulation Studies -- 8.6 Summary -- 9. Fuzzy Preferences Incorporation into Multi-objective Optimization -- 9.1 Multi-objective Optimization and Preferences Handling -- 9.2 Evolutionary Dynamic Weighted Aggregation -- 9.3 Fuzzy Preferences Incorporation in MOO -- 9.4 Summary -- References.