Integration of Neuro-Fuzzy and Genetic Algorithms for System Identification
نویسندگان
چکیده
It is known that neuro-fuzzy system is easily stuck in local minimum. To improve these drawbacks, a two-stage algorithm combining the advantages of neuro-fuzzy and genetic algorithms (GA) is integrated in system identification. Genetic algorithms are general purposed optimization algorithms with adaptive reproduction, crossover, and mutation operators that provide a method to search optimal parameters. The purpose of this paper is mainly using genetic algorithms individually to tune weights and membership functions of neuro-fuzzy system. Integrating neuro-fuzzy system and genetic algorithms is shown the better performances comparisons than neuro-fuzzy system in system identification. By applying the neuro-fuzzy system with genetic algorithms to system identification in this paper have been very successful. Keywords—Neuro-fuzzy system, Genetic algorithms, System identification
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