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

研究成果: Conference article

19 引文 斯高帕斯(Scopus)


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.

頁(從 - 到)2763-2768
期刊Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
出版狀態Published - 2005 十二月 1
事件IEEE Systems, Man and Cybernetics Society, Proceedings - 2005 International Conference on Systems, Man and Cybernetics - Waikoloa, HI, United States
持續時間: 2005 十月 102005 十月 12


All Science Journal Classification (ASJC) codes

  • Engineering(all)