| Titre : | Non asymptotic estimation methods : a focus on the volterra and modulating functions approaches |
| Auteurs : | Rania Tafat, Auteur ; Messaoud Chakir, Directeur de thèse |
| Type de document : | document électronique |
| Editeur : | [S.l.] : [s.n.], 2020 |
| Format : | 1 fichier PDF (6.3Mo) / ill. |
| Note générale : |
Mode d'accès : accès au texte intégral par intranet.
Mémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2020 Bibliogr. f. 101 - 108 |
| Langues : | Anglais |
| Index. décimale : | PA01620 |
| Tags : | Non-asymptotic estimators Volterra observers Modulating functions based method. |
| Résumé : |
In this work, we present two non-asymptotic integration transform based estimation methods: the Volterra and modulating functions approaches. We explain the design and reproduce both of the robust Volterra observer of a biased sinusoidal signal and Volterra differentiator. We contribute to the Volterra differentiator by constructing a novel bivariate kernel functions family in order to extend the approach to the noisy scenario and obtain promising results. We also propose a novel type of pseudo-modulating functions that are randomized, relax the differentiability condition and test them on a simple ODE parameter estimation in both noise-free and noisy cases where we obtain a maximum error of 5%. At last, we use the modulating functions based method to estimate the arterial blood flow and Windkessel 2-Element parameter first with analytically generated blood pressure and then using a database and conclude by underlying the data-sensitivity of the method. |
Exemplaires (1)
| Cote | Support | Localisation | Section | Disponibilité | Spécialité | Etat_Exemplaire |
|---|---|---|---|---|---|---|
| PA01620 | Ressources électroniques | Bibliothèque centrale | Projet Fin d'Etudes | Disponible | Automatique | Téléchargeable |
Documents numériques (1)
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TAFAT.Rania URL
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