A New Fuzzy Identification Approach Using Support Vector Regression and Immune Clone Selection Algorithm

摘要：

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.

关键词： Immune clone selection algorithm Fuzzy system identification Positive definite reference function TS fuzzy rule Support vector regression

作者: WenJie Tian Lan Ai Yu Geng JiCheng Liu

作者单位: Automation Institute, BEIJING Union University, BEIJING, China, 100101

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 1234-1239

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）