A new fuzzy identification approach using support vector regression and immune clone selection algorithm

Authors:
WenJie Tian;Lan Ai;Yu Geng;JiCheng Liu
Affiliations:
Automation Institute, BEIJING Union University, Beijing, China;Automation Institute, BEIJING Union University, Beijing, China;Automation Institute, BEIJING Union University, Beijing, China;Automation Institute, BEIJING Union University, Beijing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

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Abstract

A new fuzzy identification approach using support vector regression (SVR) and immune clone selection algorithm (ICSA) is presented in this paper. Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR. Then an improved ICSA is developed for parameters selection of SVR, in which the number of support vectors and regression accuracy are regarded simultaneously to guarantee the conciseness of the constructed fuzzy model. Finally, a set of TS fuzzy rules can be extracted from the SVR directly. Simulation results show that the resulting fuzzy model not only costs less fuzzy rules, but also possesses good generalization ability.