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 Takagi-Sugeno (TS) fuzzy models. We show the properties of the identification procedure in a waste-water treatment database.
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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: Gelbukh, A., de Albornoz, Á., Terashima-Marín, H. (eds) MICAI 2005: Advances in Artificial Intelligence. MICAI 2005. Lecture Notes in Computer Science(), vol 3789. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11579427_50
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DOI: https://doi.org/10.1007/11579427_50
Publisher Name: Springer, Berlin, Heidelberg
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