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Title: Fuzzy functions with support vector machines
Authors: Çelikyılmaz, Aslı
Türkşen, İsmail Burhan
Keywords: fuzzy system modeling
Support vector machines
fuzzy functions
support vector regression
data analysis
Issue Date: 2007
Publisher: Elsevier Science Inc
Abstract: A new fuzzy system modeling (FSM) approach that identifies the fuzzy functions using support vector machines (SVM) is proposed. This new approach is structurally different from the fuzzy rule base approaches and fuzzy regression methods. It is a new alternate version of the earlier FSM with fuzzy functions approaches. SVM is applied to determine the support vectors for each fuzzy cluster obtained by fuzzy c-means (FCM) clustering algorithm. Original input variables, the membership values obtained from the FCM together with their transformations form a new augmented set of input variables. The performance of the proposed system modeling approach is compared to previous fuzzy functions approaches, standard SVM, LSE methods using an artificial sparse dataset and a real-life non-sparse dataset. The results indicate that the proposed fuzzy functions with support vector machines approach is a feasible and stable method for regression problems and results in higher performances than the classical statistical methods. (C) 2007 Elsevier Inc. All rights reserved.
ISSN: 0020-0255
Appears in Collections:Endüstri Mühendisliği Bölümü / Department of Industrial Engineering
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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