A new identification method for linguistic fuzzy models

This paper presents a method for deriving a linguistic fuzzy model from an already identified fuzzy linear model. The approach is based on the novel concept of complementary fuzzy partition which is derived from the partition of a fuzzy linear model. It combines a well established identification method for fuzzy linear models with a good semantic interpretation capabilities of linguistic fuzzy models. The method is applied to the identification of a linguistic fuzzy model of a highly nonlinear process. It is shown that along with the semantic meaning the global numerical accuracy is also improved, compared to the original fuzzy linear model.<
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