Locally Weighted LS-SVM for Fuzzy Nonlinear Regression with Fuzzy Input-Output

Authors:
Dug Hun Hong;Changha Hwang;Jooyong Shim;Kyung Ha Seok
Affiliations:
Department of Mathematics, Myongji University, Kyunggido 449-728, South Korea;Corresponding Author, Division of Information and Computer Science, Dankook University, Seoul 140-714, South Korea;Department of Applied Statistics, Catholic University of Daegu, Kyungbuk 702-701, South Korea;Department of Data Science, Inje University, Kyungnam 621-749, South Korea
Venue:
Computational Intelligence and Security
Year:
2007

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Abstract

This paper deals with new regression method of predicting fuzzy multivariable nonlinear regression models using triangular fuzzy numbers. The proposed method is achieved by implementing the locally weighted least squares support vector machine regression where the local weight is obtained from the positive distance metric between the test data and the training data. Two types of distance metrics for the center and spreads are proposed to treat the nonlinear regression for fuzzy inputs and fuzzy outputs. Numerical studies are then presented which indicate the performance of this algorithm.