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Sur les modèles flous adaptatifs dynamiques

Abstract : This work deals with the proposition of an adaptive fuzzy model with dynamical membership functions. The identification of the parameters of these membership functions is performed by a on-line reinforcement learning-based algorithm. This approach takes into account the system variables dynamic by incorporating the mean value and the variance, at time t, of the input and output variables of the fuzzy model into its membership functions.By this way, the fuzzy sets associated to the fuzzy variables, are relocated on the domain of discourse according to the sampled mean and variance values; thus, a disjointed partition of the fuzzy sets of the fuzzy model could be avoid. The dynamical property of the proposed fuzzy models is an asset in fuzzy control problems in case of time-varying nonlinear systems, for example. Classical examples related to the identification of time-varying nonlinear functions show the capabilities of the dynamical fuzzy models. An application to predictive control has been developed using the fuzzy model as one step ahead predictor and the reinforcement learning in the optimization problem of this type of control scheme. Finally, a discussion about the use of the information provided by the dynamical membership functions is presented in order to accomplish diagnosis and supervision tasks at upper levels.
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Contributor : Emilie Marchand <>
Submitted on : Wednesday, August 31, 2005 - 1:59:52 PM
Last modification on : Thursday, June 10, 2021 - 3:05:12 AM
Long-term archiving on: : Friday, April 2, 2010 - 9:57:41 PM


  • HAL Id : tel-00010013, version 1


Mariela Cerrada Lozada. Sur les modèles flous adaptatifs dynamiques. Automatique / Robotique. INSA de Toulouse, 2003. Français. ⟨tel-00010013⟩



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