Fuzzy functions with support vector machines

• Published in: Information sciences Vol. 177; no. 23; pp. 5163 - 5177
• Main Authors: Çelikyılmaz, Aslı, Burhan Türkşen, I.
• Format: Journal Article
• Language: English
• Published: Elsevier Inc 01.12.2007
• Subjects: Data analysis; Fuzzy functions; Fuzzy system modeling; Support vector machines; Support vector regression; Support vector machines; Support vector regression; Data analysis; Fuzzy system modeling; Fuzzy functions
• ISSN: 0020-0255; 1872-6291
• DOI: 10.1016/j.ins.2007.06.022

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.