Fuzzy neural network design using support vector regression for function approximation with outliers

Chin Teng Lin, Sheng-Fu Liang, Chang Moun Yeh, Kan Wei Fan

Research output: Contribution to journalConference articlepeer-review

19 Citations (Scopus)

Abstract

A fuzzy neural network based on support vector learning mechanism for function approximation is proposed in this paper. Support vector regression (SVR) is a novel method for tackling the problems of function approximation and regression estimation based on the statistical learning theory. SVR has been shown to have robust properties against noise. A novel support-vector-regression based fuzzy neural network (SVRFNN) by integrating SVR technology into FNN is developed. The SVRFNN combines the high accuracy and robustness of support vector regression (SVR) and the efficient human-like reasoning of FNN for function approximation. Experimental results show that the proposed SVFNN for function approximation can achieve good approximation performance with drastically reduced number of fuzzy kernel functions.

Original languageEnglish
Pages (from-to)2763-2768
Number of pages6
JournalConference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
Volume3
Publication statusPublished - 2005 Dec 1
EventIEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, United States
Duration: 2005 Oct 102005 Oct 12

All Science Journal Classification (ASJC) codes

  • Engineering(all)

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