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

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