Proceedings of the KIEE Conference (대한전기학회:학술대회논문집)
- 1999.07b
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- Pages.507-509
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- 1999
Fuzzy Model Identification Using A mGA Hybrid Scheme
mGA의 혼합된 구조를 사용한 퍼지모델 동정
- Lee, Yeun-Woo (Dept. of Control & Instrumentation Eng. Kunsan Univ.) ;
- Joo, Young-Hoon (Dept. of Control & Instrumentation Eng. Kunsan Univ.) ;
- Park, Jin-Bae (Dept. of Electrical Eng. Yonsei Univ.)
- Published : 1999.07.19
Abstract
In this paper, we propose a new fuzzy model identification method that can yield a successful fuzzy rule base for fundamental approximations. The method in this paper uses a set of input-output data and is based on a hybrid messy genetic algorithm (mGA) with a fine-tuning scheme. The mGA processes variable-length strings, while standard GAs work with a fixed-length coding scheme. For successfully identifying a complex nonlinear system, we first use the mGA, which coarsely optimizes the structure and the parameters of the fuzzy inference system, and then the gradient descent method which tine tunes the identified fuzzy model. In order to demonstrate the superiority and efficiency of the proposed scheme, we finally show its application to a nonlinear approximation.
Keywords