Linguistic Approximation of TSK Fuzzy Models with Multi-objective Neuro-Evolutionary Algorithms

G. Sánchez, J.F. Sánchez, J.M. Alcaraz, F. Jiménez.

In this paper, a multi-objective constrained optimization model is proposed to improve interpretability of TSK fuzzy models. This approach allows a linguistic approximation of the fuzzy models. Three different multi-objective evolutionary algorithms (MONEA, ENORA and NSGA-II) are used together with neural network techniques. These algorithms are checked out in the approximation of a dynamic non-linear system studied in literature. The results clearly show a real ability and effectiveness of the proposed approach to find accurate and interpretable TSK fuzzy models.

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