Function approximation using robust fuzzy-GreyCMAC method
Po-Lun Chang, Ying‐Kuei Yang, Horng-Lin Shieh, Fei‐Hu Hsieh, Hen-Kung Wang · 2009
In this paper, we propose a novel GreyCMAC with robust FCM (RFCM) method 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 order to overcome the problems of function approximation for a nonlinear system with noise and outliers, a robust fuzzy clustering method (RFCM) is proposed to effectively mitigate the influence of noise and outliers and then a GreyCMAC model is used to learn the nonlinear system's features for function approximation. There are two core ideas in the proposed method: (1) The robust fuzzy c-means algorithm (RFCM) is proposed to greatly mitigate the influence of data noise and outliers; and (2) A Grey-based CMAC (GreyCMAC) is proposed to locate a given fine piecewise linear data domain by RFCM so that a neural network can be constructed for function approximation. The conducted experimental results clearly indicate that the proposed approach provides good performance.