Building interpretable fuzzy systems: a new approach to fuzzy modeling

At this article a new methodology is proposed to construct linguistically interpretable fuzzy models from input and output data. The proposed methodology includes clustering techniques to determine rules, the minimum squares method to adjust consequents and, for a sharp tuning, the descendant gradient to adjust the modal values of sets that confirm the antecedent. The antecedent partition uses triangular sets with 0.5 interpolations. The most promissory aspect in our proposal consists in achieving a great precision without sacrificing the fuzzy system interpretability. Some applications are presented to very well-known problems and fuzzy sets and the results are compared with those obtained by other authors using other techniques.