Model selection via worts-case criterion for nonlinear bounded-error estimation
Résumé
In this paper the problem of model selection for measurement purpose is studied. A new selcetion 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.
Domaines
Sciences de l'ingénieur [physics]| Origine | Fichiers produits par l'(les) auteur(s) |
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