Comparison of SVM-fuzzy modelling techniques for system identification

In recent years, the importance of the construction of fuzzy models from measured data has increased. Nevertheless, the complexity of real-life process is characterized by nonlinear and non-stationary dynamics, leaving so much classical identification techniques out of choice. In this paper, we present a comparison of Support Vector Machines (SVMs) for density estimation (SVDE) and for regression (SVR), versus traditional techniques as Fuzzy C-means and Gustafson-Kessel (for clustering) and Least Mean Squares (for regression), in order to find the parameters of Takagi-Sugeno (TS) fuzzy models. We show the properties of the identification procedure in a waste-water treatment database.