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Contribution à la séparation aveugle de sources
Paperback
Series: Omn.Univ.Europ.
Literary CriticismOperating Systems
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ISBN10: 6131571325
ISBN13: 9786131571329
Publisher: Ed Universitaires Europeennes
Published: Feb 28 2018
Pages: 180
Weight: 0.60
Height: 0.41 Width: 6.00 Depth: 9.00
Language: French
ISBN13: 9786131571329
Publisher: Ed Universitaires Europeennes
Published: Feb 28 2018
Pages: 180
Weight: 0.60
Height: 0.41 Width: 6.00 Depth: 9.00
Language: French
Dans cette thèse, nous proposons un nouvel algorithme de séparation aveugle de sources, basé sur l'optimisation de l'information mutuelle sous contraintes. Le problème d'optimisation sous contraintes est résolu par passage au problème dual. L'estimateur proposé du gradient stochastique utilise l'estimation des densités par maximum de vraisemblance dans des modèles de lois exponentielles choisis par minimisation du critère AIC. Ensuite, la méthode a été généralisée à l'ensemble des divergences entre densités de probabilité. Nous avons montré que l'algorithme utilisant la divergence particulière de Hellinger a de bonnes propriétés d'effcacité et robustesse en présence du bruit en comparaison avec l'information mutuelle. Dans le cadre de signaux cyclostationnaires, les méthodes précédentes de séparation ont été adaptées en utilisant des statistiques du second ordre. Nous illustrons les performances des algorithmes proposés pour des signaux simulés et pour des signaux réels issus de machines tournantes.
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