Genetic Algorithms For Fuzzy System Identification
Given the high degree of nonlinearity of the output of a fuzzy system, traditional linear optimization tools do have their limitations. Genetic algorithms have demonstrated to be a robust and very powerful tool to perform tasks such as the generation of fuzzy rule base, optimization of fuzzy rule bases, generation of membership functions, and tuning of membership functions (Cordón et al., 2001a). All these tasks can be considered as optimization or search processes within large solution spaces (Bastian and Hayashi, 1995) (Yuan and Zhuang, 1996) (Cordón et al., 2001b).
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