A new methodology for the minimization of a given set of fuzzy rules is presented. It is based on a novel mapping of fuzzy relations on boolean networks and exploits existing boolean synthesis algorithms. The formal consistency of the approach depends on a fuzzy semantic which easily generalizes most of the existing models. The technique has been applied to the fuzzy identification of non-linear systems, reducing the number of rules by up to an order of magnitude.
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