A new identification method for use in nonlinear prediction

  • Authors:

  • Felipe Montoya;Aldo Cipriano;María Ramos

  • Affiliations:

  • College of Engineering, Catholic University of Chile, P.O. Box 306, Santiago 22, Chile;(Correspd. E-mail: aciprian@ing.puc.cl) College of Engineering, Catholic University of Chile, P.O. Box 306, Santiago 22, Chile;College of Engineering, Catholic University of Chile, P.O. Box 306, Santiago 22, Chile

  • Venue:
  • Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
  • Year:
  • 2001

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Abstract

This paper presents a new identification method for fuzzy models used in nonlinear prediction. The structure and parameters of the fuzzy model are obtained, using input-output data, by minimization of the prediction error. The predictive capacity of the fuzzy model is compared with other linear and non-linear models analyzing an illustrative example. The results show that the new method presents a better behavior.