Comparison of SVM-fuzzy modelling techniques for system identification

  • Authors:

  • Ariel García-Gamboa;Miguel González-Mendoza;Rodolfo Ibarra-Orozco;Neil Hernández-Gress;Jaime Mora-Vargas

  • Affiliations:

  • Intelligent Systems Research Group, Tecnológico de Monterrey, Atizapán de Zaragoza, Estado de México, Mexico;Intelligent Systems Research Group, Tecnológico de Monterrey, Atizapán de Zaragoza, Estado de México, Mexico;Intelligent Systems Research Group, Tecnológico de Monterrey, Atizapán de Zaragoza, Estado de México, Mexico;Intelligent Systems Research Group, Tecnológico de Monterrey, Atizapán de Zaragoza, Estado de México, Mexico;Intelligent Systems Research Group, Tecnológico de Monterrey, Atizapán de Zaragoza, Estado de México, Mexico

  • Venue:
  • MICAI'05 Proceedings of the 4th Mexican international conference on Advances in Artificial Intelligence
  • Year:
  • 2005

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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.