Mamdani fuzzy system structure identification based on LSSVR
基于最小二乘支持向量回归机的Mamdani模糊系统结构

To design a Mamdani fuzzy system with good generalization ability in high dimensional feature space, a novel learning algorithm based on Least Squares Support Vector Regression (LSSVR) was presented in this paper. The structural risk was considered in the goal function to avoid overfitting in traditional algorithms and then the parameter estimation of a Mamdani fuzzy system was converted to a quadratic optimization problem. In the proposed algorithm, the fuzzy kernel generated by premise membership functions is proved to be a mercer kernel. Numerical experiments show that the presented algorithm improves the approximation ability and the generalization ability of Mamdani fuzzy systems.