文献类型：会议论文

中文题名：A New Fuzzy Identification Approach Using Support Vector Regression and Immune Clone Selection Algorithm

作者：WenJie Tian[1];Lan Ai[1];Yu Geng[1];JiCheng Liu[1];

机构：[1]Automation Institute, BEIJING Union University, BEIJING, China, 100101;

第一机构：北京联合大学城市轨道交通与物流学院

会议论文集：2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)论文集

会议日期：20090617

会议地点：广西桂林

主办单位：控制与决策编辑部

语种：英文

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

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.