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.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
No takes yet. Share an insight, caveat, or question.
Babuška et al. (2002) studied this question.
Synapse has enriched 2 closely related papers on similar clinical questions. Consider them for comparative context: