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

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3 Author(s)

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.

Published in:

IEEE Latin America Transactions  (Volume:5 ,  Issue: 5 )