Adaptive Filtering: Fundamentals and Applications - Couverture souple

Shukri Salman, Mohammad ; Kukrer, Osman ; Hocanin, Aykut

9783846548028: Adaptive Filtering: Fundamentals and Applications

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ISBN 10 : 3846548022 ISBN 13 : 9783846548028

Editeur : LAP LAMBERT Academic Publishing, 2011

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Adaptive filtering techniques are widely used to cope with the variations of system parameters. In FIR adaptive filtering, the filter weights are updated iteratively by minimizing the MSE of the difference between the desired response of the adaptive filter and its output. However, most of the existing adaptive filters experience many difficulties; fixed-step size which provides poor performance in highly correlated environments, high computational complexity, stability due to the inversion of the autocorrelation matrix, tracking ability in non-stationary and impulsive noise environments. The novelty of this work resides in introducing new FIR adaptive filtering algorithms. These algorithms have been proposed to overcome some of the difficulties experienced with the existing adaptive filtering techniques. These approaches use a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Avoiding the use of the inverse autocorrelation matrix, as the case of RLS algorithm, would provide more stable performance.

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�diteur: LAP LAMBERT Academic Publishing
Date d'�dition: 2011
Langue: anglais
ISBN 10: 3846548022
ISBN 13: 9783846548028
Reliure: Broch�
Nombre de pages: 104
Coordonn�es du fabricant: non disponible
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Adaptive Filtering

Mohammad Shukri Salman

Edit� par LAP LAMBERT Academic Publishing Nov 2011, 2011

ISBN 10 : 3846548022 ISBN 13 : 9783846548028

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Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Adaptive filtering techniques are widely used to cope with the variations of system parameters. In FIR adaptive filtering, the filter weights are updated iteratively by minimizing the MSE of the difference between the desired response of the adaptive filter and its output. However, most of the existing adaptive filters experience many difficulties; fixed-step size which provides poor performance in highly correlated environments, high computational complexity, stability due to the inversion of the autocorrelation matrix, tracking ability in non-stationary and impulsive noise environments. The novelty of this work resides in introducing new FIR adaptive filtering algorithms. These algorithms have been proposed to overcome some of the difficulties experienced with the existing adaptive filtering techniques. These approaches use a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Avoiding the use of the inverse autocorrelation matrix, as the case of RLS algorithm, would provide more stable performance. 104 pp. Englisch. N� de r�f. du vendeur 9783846548028

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Adaptive Filtering

Mohammad Shukri Salman|Osman Kukrer|Aykut Hocanin

Edit� par LAP LAMBERT Academic Publishing, 2011

ISBN 10 : 3846548022 ISBN 13 : 9783846548028

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Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Shukri Salman MohammadM. S. Salman has received the B.Sc., M.Sc. & Ph.D. degrees from Eastern Mediterranean University, North Cyprus in 2006, 2007 & 2011, respectively, all in Electrical Engineering. He served as a reviewer of many S. N� de r�f. du vendeur 5498270

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Adaptive Filtering

Mohammad Shukri Salman

Edit� par LAP LAMBERT Academic Publishing Nov 2011, 2011

ISBN 10 : 3846548022 ISBN 13 : 9783846548028

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Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Adaptive filtering techniques are widely used to cope with the variations of system parameters. In FIR adaptive filtering, the filter weights are updated iteratively by minimizing the MSE of the difference between the desired response of the adaptive filter and its output. However, most of the existing adaptive filters experience many difficulties; fixed-step size which provides poor performance in highly correlated environments, high computational complexity, stability due to the inversion of the autocorrelation matrix, tracking ability in non-stationary and impulsive noise environments. The novelty of this work resides in introducing new FIR adaptive filtering algorithms. These algorithms have been proposed to overcome some of the difficulties experienced with the existing adaptive filtering techniques. These approaches use a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Avoiding the use of the inverse autocorrelation matrix, as the case of RLS algorithm, would provide more stable performance.VDM Verlag, Dudweiler Landstra�e 99, 66123 Saarbr�cken 104 pp. Englisch. N� de r�f. du vendeur 9783846548028

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Adaptive Filtering : Fundamentals and Applications

Mohammad Shukri Salman

Edit� par LAP LAMBERT Academic Publishing, 2011

ISBN 10 : 3846548022 ISBN 13 : 9783846548028

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Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Adaptive filtering techniques are widely used to cope with the variations of system parameters. In FIR adaptive filtering, the filter weights are updated iteratively by minimizing the MSE of the difference between the desired response of the adaptive filter and its output. However, most of the existing adaptive filters experience many difficulties; fixed-step size which provides poor performance in highly correlated environments, high computational complexity, stability due to the inversion of the autocorrelation matrix, tracking ability in non-stationary and impulsive noise environments. The novelty of this work resides in introducing new FIR adaptive filtering algorithms. These algorithms have been proposed to overcome some of the difficulties experienced with the existing adaptive filtering techniques. These approaches use a variable step-size and the instantaneous value of the autocorrelation matrix in the coefficient update equation that leads to an improved performance. Avoiding the use of the inverse autocorrelation matrix, as the case of RLS algorithm, would provide more stable performance. N� de r�f. du vendeur 9783846548028

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