Overview of methods of fuzzy system identification on T-S model
JIANG Qiang1,2,XIAO Jian1,HE Du-yi2,JIANG Wei1,WANG Meng-ling1
(1.School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, China;2.Leshan Teachers College, Leshan Sichuan 614000, China)
Abstract:There are different methodologies of fuzzy model design, which can mainly be divided into two groups: the semantic-driven modeling and data-driven modeling. Data-driven modeling became more popular for better property than semantic-driven. Fuzzy system identification is one of the main approaches of fuzzy system modeling. The accuracy of fuzzy system model relate to the result of fuzzy system identification. Fuzzy system identification includes structure identification and parameters identification.This paper reviewed the state of theorem and methods of fuzzy system identification briefly.Disscussed widely used methods of fuzzy system identification,including those based on subtractive clustering, multi-resolution analysis, support vector machine, kernel function, particle swarm and parallel genetic algorithm etc.Analyzed the characteristics of the methods,and outlined future research directions of fuzzy system identification. ......