Advanced Fuzzy Systems Design and Applic
β Yaochu Jin
π Library
π
2003
π Physica
π English
β¦ LIBER β¦
π
Advanced Fuzzy Systems Design and Applications
β Scribed by Dr. Yaochu Jin (auth.)
- Publisher
- Physica-Verlag Heidelberg
- Year
- 2003
- Tongue
- English
- Leaves
- 275
- Series
- Studies in Fuzziness and Soft Computing 112
- Edition
- 1
- Category
- Library
β¬ Acquire This Volume
No coin nor oath required. For personal study only.
β¦ Synopsis
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 netΒ works and evolutionary algorithms based fuzzy rule generation. Besides the approximation accuracy, special attention has been paid to the interpretabilΒ 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 interpretabilΒ 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.
β¦ Table of Contents
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
β¦ Subjects
Artificial Intelligence (incl. Robotics); Data Structures, Cryptology and Information Theory; Algorithm Analysis and Problem Complexity
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