SIGRIST, Zoé; LEGRAND, Pierrick; GRIVEL, Eric; ALCOVERRO, Benoit

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SIGRIST, Zoé
Laboratoire de l'intégration, du matériau au système [IMS]

LEGRAND, Pierrick
Institut de Mathématiques de Bordeaux [IMB]
Advanced Learning Evolutionary Algorithms [ALEA]

GRIVEL, Eric
Laboratoire de l'intégration, du matériau au système [IMS]

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Ce document a été publié dans

Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, Signal Processing Conference (EUSIPCO), 2012 Proceedings of the 20th European, EUSIPCO 2012 - 20th European Signal Processing Conference, 2012-08-27, Bucharest. 2012-08-27p. 2114-2118

IEEE

Résumé en anglais

This paper deals with the identi cation of a nonlinear system modelled by a nonlinear output error (NOE) model when the system output is disturbed by an additive zero-mean white Gaussian noise. In that case, standard on-line or off-line least squares methods may lead to poor results. Here, our approach is based on evolutionary algorithms. Although their computational cost can be higher than the above methods, these algorithms present some advantages, which often lead to an effortless optimisation. Indeed, they do not need an elaborate formalisation of the problem. When their parameters are correctly tuned, they avoid to get stuck at a local optimum. To take into account the in uence of the additive noise, we investigate different approaches and we suggest a whole protocol including the selection of a tness function and a stop rule. Without loss of generality, simulations are provided for two nonlinear systems and various signal-to-noise ratios.< Réduire

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Unités de recherche

Institut de Mathématiques de Bordeaux (IMB) - UMR 5251