Linguistic Approximation of TSK Fuzzy Models with Multi-objective Neuro-Evolutionary Algorithms
Gracia Sánchez · 2008
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 dierent 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 eectiveness of the proposed approach to find accurate and interpretable TSK fuzzy models.