Takagi-Sugeno fuzzy modeling incorporating input variables selection

Hadjili, ML;Wertz, Vincent
(2002) IEEE Transactions on Fuzzy Systems — Vol. 10, n° 6, p. 728-742 (2002)

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  • Hadjili, ML
    Author
  • Wertz, VincentUCLouvain
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Abstract
Fuzzy models, especially Takagi-Sugeno (T-S) fuzzy models, have received particular attention in the area of nonlinear modeling due to their capability to approximate any nonlinear behavior. Based only on measured data without any prior knowledge, there is no systematic way to obtain a T-S fuzzy model with a simple structure and sufficient accuracy. The main idea discussed in this paper is to reduce the complexity of T-S fuzzy models by estimating an optimal number of fuzzy rules and selecting relevant inputs as antecedent variables independently of the selection of consequent regressors. A systematic procedure is proposed here and illustrated on static and dynamical nonlinear systems.
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Hadjili, M., & Wertz, V. (2002). Takagi-Sugeno fuzzy modeling incorporating input variables selection. IEEE Transactions on Fuzzy Systems, 10(6), 728-742. https://doi.org/10.1109/TFUZZ.2002.805897 (Original work published 2002)