Model selection via worst-case criterion for nonlinear bounded-error estimation

Abstract : In this paper the problem of model selection for measurement purpose is studied. A new selection procedure in a deterministic framework is proposed. The problem of nonlinear bounded-error estimation is viewed as a set inversion procedure. As each candidate model structure leads to a specific set of admissible values of the measurement vector, the worts-case criterion is used to select the optimal model. The selection procedure is applied to a real measurement problem, grooves dimensioning using Remote Field Eddy Current (RFEC) inspection.
Type de document :
Article dans une revue
IEEE Instrumentation and Measurement Magazine, Institute of Electrical and Electronics Engineers, 2000, 49 (3), pp.653-658
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00844852
Contributeur : Marie-Françoise Gerard <>
Soumis le : mardi 16 juillet 2013 - 09:50:02
Dernière modification le : mercredi 20 février 2019 - 14:39:56
Document(s) archivé(s) le : jeudi 17 octobre 2013 - 04:15:00

Fichier

IEEEmodelSelect_LISA2000.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00844852, version 1

Citation

S. Brahim-Belhouari, Michel Kieffer, G. Fleury, Luc Jaulin, Eric Walter. Model selection via worst-case criterion for nonlinear bounded-error estimation. IEEE Instrumentation and Measurement Magazine, Institute of Electrical and Electronics Engineers, 2000, 49 (3), pp.653-658. 〈hal-00844852〉

Partager

Métriques

Consultations de la notice

367

Téléchargements de fichiers

168