1 - Le filtrage adaptatif transverse

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dc.contributor.author MACCHI (O.) en_US
dc.contributor.author BELLANGER (M.) en_US
dc.date.accessioned 2005-07-20T12:55:23Z
dc.date.available 2005-07-20T12:55:23Z
dc.date.issued 1988 en_US
dc.identifier.citation Traitement du Signal [Trait. Signal], 1988, Vol. 5, N° 3, p. 115-132 en_US
dc.identifier.issn 0765-0019 en_US
dc.identifier.uri http://hdl.handle.net/2042/1659
dc.description This paper presents a tutorial on the gradient (G) and recursive least squares (RLS) algorithme, both commonly used in adaptive transversal filters for estimating a linear model . It is shown how the algorithms utilize recursivity ta realize at the saine time the averaging involved in the optimisation criterion as well as the minimization. In steady state both algorithms can be viewed as minimizing the saure mean square error criterion. Then they have equivalent performances : the saure residual fluctuations up to an equivalence between the adaptation step-size t of (G) and the forgetting rate (1-w) of (RLS) . Due to the criterion itself emphasizing adaptation speed, it is the transient period which exhibits the (RLS) superiority . This is essentially at the price of a four times higher complexity and of difficulties with controling numerical effects, related (in part) to the presence of divisions that are not in (G) . A general methodology for decoupling the transient effects and permanent fluctuations is given ; the latter are due to the measurement noise disturbing the model . The more general problem of tracking the model variations is treated with that methodology and we emphasize the différence between the problems of tracking and of squeezing the transient period for a fixed model : the corresponding adaptation step-sizes differ significantly .
dc.description.abstract Synthèse sur les algorithmes du gradient et des moindres carrés rapides. En régime permanent, ils ont des performances équivalentes. C'est en régime transitoire que les moindres carrés sont supérieurs; ceci au prix d'une complexité environ quatre fois supérieure et de difficultés de contrôle des variables numériques en_US
dc.format.extent 51964 bytes
dc.format.mimetype application/pdf
dc.language.iso en_US
dc.publisher GRETSI, Saint Martin d'Hères, France en_US
dc.relation.ispartofseries Traitement du Signal
dc.rights http://irevues.inist.fr/utilisation en_US
dc.source Traitement du Signal [Trait. Signal], ISSN 0765-0019, 1988, Vol. 5, N° 3, p. 115-132 en_US
dc.subject.cnrs Filtrage en_US
dc.subject.cnrs Filtrage adaptatif en_US
dc.subject.cnrs Algorithme rapide en_US
dc.subject.cnrs Méthode gradient en_US
dc.subject.cnrs Méthode moindre carré en_US
dc.subject.cnrs Etude comparative en_US
dc.subject.cnrs Erreur quadratique moyenne en_US
dc.subject.cnrs Matrice covariance en_US
dc.subject.cnrs Régime transitoire en_US
dc.subject.cnrs Prédiction en_US
dc.subject.cnrs Coefficient pondération en_US
dc.title 1 - Le filtrage adaptatif transverse en_US
dc.title.alternative Transversal adaptive filtering en_US
dc.type Article en_US
dc.contributor.affiliation ESE, lab. signaux systèmes, Gif-sur-Yvette 91190 en_US

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