Evolutionary design of fuzzy rule base for nonlinear system modeling and control

In designing fuzzy models and controllers, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. The paper presents an approach to the evolutionary design of an optimal fuzzy rule base for modeling...

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Bibliographic Details
Published in:IEEE transactions on fuzzy systems 2000-02, Vol.8 (1), p.37-45
Main Authors: Sin-Jun Kang, Chun-Hee Woo, Hee-Soo Hwang, Woo, K.B.
Format: Article
Language:English
Subjects:
Calculus
Control system synthesis
Design engineering
Design optimization
Evolutionary
Evolutionary design method
Fuzzy
Fuzzy control
Fuzzy logic
Fuzzy set theory
Fuzzy sets
Fuzzy systems
Genetic programming
Mathematical models
Nonlinear control systems
Nonlinear systems
Tasks
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Summary:In designing fuzzy models and controllers, we encounter a major difficulty in the identification of an optimized fuzzy rule base, which is traditionally achieved by a tedious trial-and-error process. The paper presents an approach to the evolutionary design of an optimal fuzzy rule base for modeling and control. Evolutionary programming is used to simultaneously evolve the structure and the parameter of fuzzy rule base for a given task. To check the effectiveness of the suggested approach, four numerical examples are examined. The performance of the identified fuzzy rule bases is demonstrated.
ISSN:1063-6706
1941-0034
DOI:10.1109/91.824766