Tuning of fuzzy inference systems through unconstrained optimization techniques

• Autor(a) principal: Flauzino, Rogerio A. [UNESP]
• Data de Publicação: 2003
• Outros Autores: Ulson, Jose Alfredo Covolan [UNESP], Da Silva, Ivan Nunes [UNESP]
• Tipo de documento: Artigo de conferência
• Idioma: eng
• Título da fonte: Repositório Institucional da UNESP
• Texto Completo: http://www.wseas.us/e-library/conferences/brazil2002/papers/449-261.pdf; http://hdl.handle.net/11449/67558
• topic: Chaos theory; Error analysis; Mathematical models; Matrix algebra; Membership functions; Problem solving; Time series analysis; Chaotic time series estimation; Fuzzy inference systems; Intrinsic parameters; Mandani architecture; Fuzzy sets

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 intrinsic parameters of the membership function and the weights 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 an estimation of time series. More specifically, the Mackey-Glass chaotic time series estimation is used for the validation of the proposed methodology.