Fuzzy nonlinear regression model based on LS-SVM in feature space

Authors:
Dug Hun Hong;Changha Hwang
Affiliations:
Department of Mathematics, Myongji University, Yongin Kyunggido, South Korea;Corresponding Author, Division of Information and Computer Science, Dankook University, Yongsan Seoul, South Korea
Venue:
FSKD'06 Proceedings of the Third international conference on Fuzzy Systems and Knowledge Discovery
Year:
2006

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Abstract

This paper presents a new method of estimating fuzzy multivariable nonlinear regression model for fuzzy input and fuzzy output data. This estimation method is obtained by constructing a fuzzy linear regression based on least squares support vector machine(LS-SVM) in a high dimensional feature space for the data set with fuzzy inputs and fuzzy output. Experimental results are then presented which indicate the performance of this algorithm.