In this paper we present different approaches to the problem offuzzy rules extraction by using a combination of fuzzy clustering andgenetic algorithms as the main tools. This combination of techniqueslet us define a hybrid system by which we can have differentapproaches in a fuzzy modeling process. For example, we can obtain afirst approximation to the fuzzy rules that describe the systembehavior represented by a collection of raw data, without anyassumption about the structure of the data using a fuzzy clusteringtechnique, and subsequently, these rues can be tuned using a geneticalgorithm. Alternatively, this genetic algorithm can be used in orderto generate and tune the fuzzy rules directly from the data with orwithout some priori information. Finally, their performances arecompared.
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