Support Vector Machine Regression for a Gaussian Fuzzy Model

Support vector machine(SVM) has been very successful in pattern recognition and function estimation problems for crisp data. In this paper, we propose SVM approach to evaluating fuzzy regression model with multiple crisp inputs and a Gaussian fuzzy output. The proposed algorithm here is model-free method in the sense that we do not need assume the underlying model function. This algorithm is robust for the estimation of fuzzy linear and nonlinear regression models, especially when outliers are present. Numerical examples are given to detail the effectiveness of this approach.