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.
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  • HAL Id : hal-00844852, version 1

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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⟩

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