Efficient parametric adjustment of fuzzy inference system using unconstrained optimization

Authors:
Ivan Nunes Da Silva;Rogerio Andrade Flauzino
Affiliations:
University of São Paulo, Department of Electrical Engineering, São Carlos, SP, Brazil;São Paulo State University, Department of Production Engineering, Bauru, SP, Brazil
Venue:
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Year:
2007

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Abstract

This paper presents a new methodology for the adjustment of fuzzy inference systems, which uses technique based on error back-propagation method. The free parameters of the fuzzy inference system, such as its intrinsic parameters of the membership function and the weights of the inference rules, are automatically adjusted. This methodology is interesting, not only for the results presented and obtained through computer simulations, but also for its generality concerning to the kind of fuzzy inference system used. Therefore, this methodology is expandable either to the Mandani architecture or also to that suggested by Takagi-Sugeno. The validation of the presented methodology is accomplished through estimation of time series and by a mathematical modeling problem. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology.