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This study deals with the robust tuning of a fuzzy model with uncertain data in a deterministic framework. We consider the on-line identification of an interpretable fuzzy model without making any assumption and requiring a priori knowledge of upper bounds, statistics, and distribution of data uncertainties. The tuning of the fuzzy model parameters is based on the robust (min-max) solution of a regularized least-squares constrained optimization problem. The simulation studies are carried out to show the better performance of our approach.