Considering the measurement noise for a nonlinear system identification with evolutionary algorithms.

Zoé Sigrist 1 Pierrick Legrand 2, 3 Eric Grivel 4 Benoit Alcoverro
1 Signal, DESTIN
IMS - Laboratoire de l'intégration, du matériau au système
3 ALEA - Advanced Learning Evolutionary Algorithms
Inria Bordeaux - Sud-Ouest, UB - Université de Bordeaux, CNRS - Centre National de la Recherche Scientifique : UMR5251
Abstract : 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.
Type de document :
Communication dans un congrès
EUSIPCO 2012, Aug 2012, Bucharest, Romania. 2012
Liste complète des métadonnées

https://hal.inria.fr/hal-00757231
Contributeur : Pierrick Legrand <>
Soumis le : lundi 26 novembre 2012 - 14:58:01
Dernière modification le : jeudi 11 janvier 2018 - 06:27:11

Identifiants

  • HAL Id : hal-00757231, version 1

Citation

Zoé Sigrist, Pierrick Legrand, Eric Grivel, Benoit Alcoverro. Considering the measurement noise for a nonlinear system identification with evolutionary algorithms.. EUSIPCO 2012, Aug 2012, Bucharest, Romania. 2012. 〈hal-00757231〉

Partager

Métriques

Consultations de la notice

206