Fuzzy system identification based on support vector regression and genetic algorithm

摘要：

A new fuzzy identification approach using support vector regression (SVR) and genetic algorithm (GA) is presented in this paper.Firstly positive definite reference function is utilized to construct a qualified Mercer kernel for SVR.Then an improved GA 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： fuzzy system identification positive definite reference function support vector regression genetic algorithm.

作者: Wei Li Yupu Yang Zhong Yang Changying Zhang

作者单位: Department of Automation,Shanghai Jiao Tong University,Shanghai,China

会议类型: 国际会议

会议名称: International Conference on Modelling,Identification and Control(模拟、鉴定、控制国际会议)

会议地点: 上海

会议语种:英文

在线出版日期: 2008-06-29（万方平台首次上网日期，不代表论文的发表时间）