Function approximation using robust fuzzy-GreyCMAC method Online publication date: Sat, 21-Mar-2015
by Hen-Kung Wang; Jonq-Chin Hwang; Po-Lun Chang; Fei-Hu Hsieh
International Journal of Modelling, Identification and Control (IJMIC), Vol. 14, No. 4, 2011
Abstract: A novel GreyCMAC model combined with robust FCM (RFCM) approach for function approximation is proposed in this paper. In order to overcome the problems of function approximation for a non-linear system with noises and outliers, a robust fuzzy clustering method is proposed to mitigate the influence of noises and outliers and then a novel GreyCMAC model is proposed to learn the non-linear system's features for fast and accurate function approximation. There are two core ideas in the proposed approach: 1) the robust fuzzy c-means algorithm (RFCM) is proposed to greatly mitigate the influence of data noises and outliers; 2) a grey-based CMAC (GreyCMAC) is proposed to locate a given fine piecewise linear data domain by RFCM so that a CMAC neural network can be constructed for function approximation. The conducted simulation results clearly indicate that the proposed approach provides good performance.
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