An improved training algorithm of T-S model HHFNN based on ridge regression function

Abstract

a new training algorithm for hierachical hybrid fuzzy -neural network (HHFNN) based on Takagi -Sugeno (T-S) fuzzy system is proposed in this paper. Triangular membership function is adopted. And to reduce the strong interaction among discrete input variables, coefficient contraction method is employed; ridge regression function is used in the THEN parts of fuzzy rules. At last, pyrimidines medical data is used in simulations; results show that our new algorithm gets an advantage in accuracy over the existing training algorithms for HHFNN and standard BP algorithm.