Data-Driven Identification Algorithms for Automatic Determination of Interpretable Fuzzy Models

This article presents a new methodology to obtain fuzzy models linguistically interpretable from input and output data. The proposed methodology includes the class determination and rules generation algorithms, as long as the partition sum-1 of the input variables: shape, number and distribution of the fuzzy sets. The most promising issue on our proposal is represented by the equilibrium between precision and interpretability of the model. Applications to well-known problems and data sets are presented and compared with the results of other authors using different techniques.