DocumentCode :
3434889
Title :
Function approximation using robust fuzzy-GreyCMAC method
Author :
Chang, Po-lun ; Yang, Ying-kuei ; Shieh, Horng-lin ; Hsieh, Fei-hu ; Wang, Hen-kung
Author_Institution :
Dept. of Electr. Eng., Lunghwa Univ., Lunghwa
fYear :
2009
fDate :
10-13 Feb. 2009
Firstpage :
1
Lastpage :
6
Abstract :
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.
Keywords :
cluster approximation; function approximation; fuzzy neural nets; nonlinear functions; GreyCMAC model; function approximation; neural network; nonlinear functions; piecewise linear data domain; robust fuzzy clustering method; Approximation algorithms; Clustering algorithms; Clustering methods; Convergence; Function approximation; Fuzzy neural networks; Fuzzy systems; Neural networks; Noise robustness; Nonlinear systems; GreyCMAC; noises and outliers; robust fuzzy c-means (RFCM);
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Industrial Technology, 2009. ICIT 2009. IEEE International Conference on
Conference_Location :
Gippsland, VIC
Print_ISBN :
978-1-4244-3506-7
Electronic_ISBN :
978-1-4244-3507-4
Type :
conf
DOI :
10.1109/ICIT.2009.4939700
Filename :
4939700
Link To Document :
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