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- W24712081 abstract "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." @default.
- W24712081 created "2016-06-24" @default.
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- W24712081 date "2008-01-01" @default.
- W24712081 modified "2023-09-27" @default.
- W24712081 title "Linguistic Approximation of TSK Fuzzy Models with Multi-objective Neuro-Evolutionary Algorithms" @default.
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