New genetic-based approach to generate fuzzy rules from numerical data

Optimization of fuzzy logic controller by genetic algorithm is a very active research area. This paper develops a new genetic-based method to generate fuzzy rules from numerical data; the fuzzy rules and fuzzy membership functions can be optimized simultaneously in the algorithm. An application to t...

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Bibliographic Details
Main Authors: Jun Zhu, Run-Sheng Yang, Shi-Yu Sun
Format: Conference Proceeding
Language:English
Subjects:
Data engineering
Educational institutions
Fuzzy control
Fuzzy logic
Genetic algorithms
Genetic engineering
Optimization methods
Sun
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Summary:Optimization of fuzzy logic controller by genetic algorithm is a very active research area. This paper develops a new genetic-based method to generate fuzzy rules from numerical data; the fuzzy rules and fuzzy membership functions can be optimized simultaneously in the algorithm. An application to truck backer-upper control is presented. The performance of this new method is compared with a non-optimized method, and shows that the new method has a better performance.
DOI:10.1109/WCICA.2000.862777