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dc.contributor.authorTafat, Rania-
dc.contributor.otherChakir, Messaoud, Directeur de thèse-
dc.date.accessioned2020-12-20T11:18:09Z-
dc.date.available2020-12-20T11:18:09Z-
dc.date.issued2020-
dc.identifier.otherEP00070-
dc.identifier.urihttp://repository.enp.edu.dz/xmlui/handle/123456789/1107-
dc.descriptionMémoire de Projet de Fin d’Études : Automatique : Alger, École Nationale Polytechnique : 2020fr_FR
dc.description.abstractIn 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.fr_FR
dc.language.isoenfr_FR
dc.subjectNon-asymptotic estimatorsfr_FR
dc.subjectVolterra observersfr_FR
dc.subjectModulating functions based methodfr_FR
dc.titleNon asymptotic estimation methods : a focus on the volterra and modulating functions approachesfr_FR
dc.typeThesisfr_FR
Collection(s) :Département Automatique

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