Weighted principal component analysis for Wiener system identification – Regularization and non-Gaussian excitations

Qinghua Zhang 1 Vincent Laurain 2 Jiandong Wang 3
1 I4S - Statistical Inference for Structural Health Monitoring
IFSTTAR/COSYS - Département Composants et Systèmes, Inria Rennes – Bretagne Atlantique
2 CRAN - Centre de Recherche en Automatique de Nancy
3 Peking University [Beijing]
Abstract : Finite impulse response (FIR) Wiener systems driven by Gaussian inputs can be efficiently identified by a well-known correlation-based method, except those involving even static nonlinearities. To overcome this deficiency, another method based on weighted principal component analysis (wPCA) has been recently proposed. Like the correlation-based method, the wPCA is designed to estimate the linear dynamic subsystem of a Wiener system without assuming any parametric form of the nonlinearity. To enlarge the applicability of this method, it is shown in this paper that high order FIR approximation of IIR Wiener systems can be efficiently estimated by controlling the variance of parameter estimates with regularization techniques. The case of non-Gaussian inputs is also studied by means of importance sampling.
Keywords : block-oriented nonlinear system Wiener system identification principal component analysis
Type de document :
Communication dans un congrès
17th IFAC Symposium on System Identification, SYSID 2015, Oct 2015, Beijing, France. 2015
Liste complète des métadonnées

Littérature citée [20 références]  Voir  Masquer  Télécharger
https://hal.inria.fr/hal-01232183
Contributeur : Qinghua Zhang <>
Soumis le : lundi 23 novembre 2015 - 10:54:34
Dernière modification le : mercredi 11 avril 2018 - 02:00:46
Document(s) archivé(s) le : vendredi 28 avril 2017 - 22:20:37

Fichier

SYSID2015_Wiener.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-01232183, version 1

Collections

CRAN | INRIA | UNIV-LORRAINE | IFSTTAR | UNAM | INRIA-RENGRE

Citation

Qinghua Zhang, Vincent Laurain, Jiandong Wang. Weighted principal component analysis for Wiener system identification – Regularization and non-Gaussian excitations. 17th IFAC Symposium on System Identification, SYSID 2015, Oct 2015, Beijing, France. 2015. 〈hal-01232183〉

Exporter

BibTeX TEI DC DCterms EndNote

Partager

Métriques

Consultations de la notice

458

Téléchargements de fichiers

125

Contact


archive-ouverte@inria.fr