Please use this identifier to cite or link to this item: http://hdl.handle.net/11455/44332
標題: A self-organizing TS-type fuzzy network with support vector learning and its application to classification problems
作者: Juang, C.F.
莊家峰
Chiu, S.H.
Chang, S.W.
關鍵字: composite kernel;fuzzy clustering;fuzzy neural network (FNN);skin;color segmentation;support vector machine (SVM);Takagi-Sugeno;(TS)-type fuzzy systems;neural-network;machines;systems;color
Project: Ieee Transactions on Fuzzy Systems
期刊/報告no:: Ieee Transactions on Fuzzy Systems, Volume 15, Issue 5, Page(s) 998-1008.
摘要: 
A self-organizing Takagi-Sugeno (TS)-type fuzzy network with support vector learning (SOTFN-SV) is proposed in this paper. The proposed SOTFN-SV is inspired by analysis of TS-type fuzzy systems and composite-kernel support vector machine (SVM). SOTFN-SV is a fuzzy system constructed by the hybridization of fuzzy clustering and SVM. The antecedent part of SOTFN-SV is generated via fuzzy clustering of the input data, and then SVM is used to tune the consequent part parameters to give the network better generalization performance. For demonstration, SOTFN-SV is applied to several classification problems, especially the skin color classification problem. In the skin color classification application, each color pixel is represented by hue and saturation (HS) color space. To represent color information by histogram as accurately as possible, a nonuniform partition of HS space is proposed. For comparison, SVMs and other fuzzy systems trained by SVM or neural networks are applied to the same classification problems. The advantages of SOTFN-SV are verified by comparisons with the results of these methods.
URI: http://hdl.handle.net/11455/44332
ISSN: 1063-6706
DOI: 10.1109/tfuzz.2007.894980
Appears in Collections:電機工程學系所

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