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Article Dans Une Revue Automatica Année : 2005

Regularization aspects in continuous-time model identification

Résumé

This paper presents an analysis of some regularization aspects in continuous-time model identification. The study particulary focuses on linear filter methods and shows that filtering the data before estimating their derivatives corresponds to a regularized signal derivative estimation by minimizing a compound criterion whose expression is given explicitly. A new structure based on a null phase filter corresponding to a true regularization filter is proposed and allows to discuss the filter phase effects on parameter estimation by comparing its performances with those of the Poisson filter-based methods. Based on this analysis, a formulation of continuous-time model identification as a joint system input–output signal and model parameter estimation is suggested. In this framework, two linear filter methods are interpreted and a compound criterion is proposed in which the regularization is ensured by a model fitting measure, resulting in a new regularization filter structure for signal estimation.
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hal-00456203 , version 1 (21-02-2022)

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Paternité - Pas d'utilisation commerciale

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Saïd Moussaoui, David Brie, Alain Richard. Regularization aspects in continuous-time model identification. Automatica, 2005, 41 (2), pp.197-208. ⟨10.1016/j.automatica.2004.10.008⟩. ⟨hal-00456203⟩
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