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
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
文章連結: http://dx.doi.org/10.1109/tfuzz.2007.894980
Appears in Collections:電機工程學系所

文件中的檔案:

取得全文請前往華藝線上圖書館



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.