A new identification method for use in nonlinear prediction

Article type: Research Article

Authors: Montoya, Felipe | Cipriano, Aldo | Ramos, María

Affiliations: College of Engineering, Catholic University of Chile, P.O. Box 306, Santiago 22, Chile

Note: [] Corresponding author. E-mail: [email protected]

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.

Journal: Journal of Intelligent & Fuzzy Systems, vol. 10, no. 3-4, pp. 131-137, 2001