Abstract
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 Takagl-Sugeno (TS) fuzzy models. We show the properties of the identification procedure in a waste-water treatment database. © Springer-Verlag Berlin Heidelberg 2005.
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CITATION STYLE
García-Gamboa, A., González-Mendoza, M., Ibarra-Orozco, R., Hernández-Gress, N., & Mora-Vargas, J. (2005). Comparison of SVM-fuzzy modelling techniques for system identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3789 LNAI, pp. 494–503). Springer Verlag. https://doi.org/10.1007/11579427_50
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