文献类型：会议论文

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

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

通讯作者：Tian, WJ[1]

机构：[1]BEIJING Union Univ, Automat Inst, Beijing 100101, Peoples R China

第一机构：北京联合大学城市轨道交通与物流学院

通讯机构：[1]corresponding author), BEIJING Union Univ, Automat Inst, Beijing 100101, Peoples R China.|[1141751]北京联合大学城市轨道交通与物流学院;[11417]北京联合大学;

会议论文集：21st Chinese Control and Decision Conference

会议日期：JUN 17-19, 2009

会议地点：Guilin, PEOPLES R CHINA

语种：英文

外文关键词：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.