Tuning of fuzzy inference systems through unconstrained optimization techniques
Rogério Andrade Flauzino, José Alfredo Covolan Ulson, Ivan Nunes da Silva · UNESP Institutional Repository (São Paulo State University) · 2003
Abstract:- This paper presents a new methodology for the adjustment of fuzzy inference systems. A novel approach, which uses unconstrained optimization techniques, is developed in order to adjust the free parameters of the fuzzy inference system, such as its own parameters of the membership function, and the weight of the inference rules. 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. More specifically, the Mackey-Glass chaotic time series is used for the validation of the proposed methodology.