A robust fuzzy CMAC for function approximation

This paper proposes a new robust fuzzy CMAC algorithm for function approximation. The advantages of CMAC neural network are fast learning convergence, capable of mapping nonlinear functions quickly due to its local generalization of weight updating. In this paper, in order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method is proposed to greatly mitigate the influence of noise and outliers and then a new CMAC learning process used to learn the nonlinear system's features for function approximation.