Hybrid kernel learning via genetic optimization for TS fuzzy system identification

摘要

This paper presents a new TS fuzzy system identification approach based on hybrid kernel learning and an improved genetic algorithm (GA). Structure identification is achieved by using support vector regression (SVR), in which a hybrid kernel function is adopted to improve regression performance. For multiple-parameter selection of SVR, the proposed GA is adopted to speed up the search process and guarantee the least number of support vectors. As a result, a concise model structure can be determined by these obtained support vectors. Then, the premise parameters of fuzzy rules can be extracted from results of SVR, and the consequent parameters can be optimized by the least-square method. Simulation results show that the resulting fuzzy model not only achieves satisfactory accuracy, but also takes on good generalization capability.