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Multi-objective optimization of TSK fuzzy models

O. Guenounou A. Belmehdi Boutaib Dahhou 1
1 LAAS-DISCO - Équipe DIagnostic, Supervision et COnduite
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : In this paper we propose a hybrid algorithm to optimize the structure of TSK type fuzzy model using backpropagation (BP) learning algorithm and non-dominated sorting genetic algorithm (NSGA-II). In a first step, BP algorithm is used to optimize the parameters of the model (parameters of membership functions and fuzzy rules). NSGA-II is used in a second phase, to optimize the number of fuzzy rules and to fine tune the parameters. A well known benchmark is used to evaluate performances of the proposed modelling approach, and compare it with other modelling approaches.
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https://hal.laas.fr/hal-02957524
Contributor : Boutaib Dahhou <>
Submitted on : Monday, October 5, 2020 - 11:31:51 AM
Last modification on : Thursday, June 10, 2021 - 3:05:47 AM

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O. Guenounou, A. Belmehdi, Boutaib Dahhou. Multi-objective optimization of TSK fuzzy models. Expert Systems with Applications, Elsevier, 2009, 36 (4), pp.7416-7423. ⟨10.1016/j.eswa.2008.09.044⟩. ⟨hal-02957524⟩

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