Sparse adaptive filtering techniques von aliyu muhammad (5 Ergebnisse)
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Taschenbuch. Zustand: Neu. Sparse Adaptive Filtering Techniques for Channel Estimation | Muhammad Lawan Aliyu (u. a.) | Taschenbuch | 76 S. | Englisch | 2015 | LAP LAMBERT Academic Publishing | EAN 9783659468759 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[do…t]de | Anbieter: preigu.
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Taschenbuch. Zustand: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Recently,sparse signal approximation has become an increasingly important research area in signal processing. It attracts a lot of interest due to its wide range of practical applications. In this work, a novel adaptive filtering algo…rithm with relative low computational complexity that is capable of exploiting the sparsity of systems is proposed. The basic idea here is, we adopt a p-norm constraint in the cost function of the variable step-size least mean square (VSSLMS) algorithm. This constrain imposes a zero attraction at each filter coefficient based on their respective relative value. Also, the convergence analysis of the proposed algorithm is presented and the stability condition is derived. The performance of the proposed algorithm has been compared to those of the Zero Attraction Least Mean Square(ZA-LMS), windowing ZA-LMS(wZA-LMS), Non-uniform Norm Constraint LMS(NNCLMS) in a system identification setting for different additive Gaussian noise(AGN), additive correlated noise(ACN)and additive impulsive noise(AIN) environments. The proposed algorithm has always shown superior performance to the others with less or comparable number of computations. 76 pp. Englisch.
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Zustand: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Aliyu Muhammad LawanMuhammad Lawan Aliyu, born in Kano, Nigeria, is a graduate of Electrical Engineering from Bayero University, Kano. He obtained his M.Sc. in Electrical and Electronic Engineering from M…evlana University Turkey with.
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Taschenbuch. Zustand: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Recently,sparse signal approximation has become an increasingly important research area in signal processing. It attracts a lot of interest due to its wide range of practical applications. In this work, a novel adaptive filtering algorith…m with relative low computational complexity that is capable of exploiting the sparsity of systems is proposed. The basic idea here is, we adopt a p-norm constraint in the cost function of the variable step-size least mean square (VSSLMS) algorithm. This constrain imposes a zero attraction at each filter coefficient based on their respective relative value. Also, the convergence analysis of the proposed algorithm is presented and the stability condition is derived. The performance of the proposed algorithm has been compared to those of the Zero Attraction Least Mean Square(ZA-LMS), windowing ZA-LMS(wZA-LMS), Non-uniform Norm Constraint LMS(NNCLMS) in a system identification setting for different additive Gaussian noise(AGN), additive correlated noise(ACN)and additive impulsive noise(AIN) environments. The proposed algorithm has always shown superior performance to the others with less or comparable number of computations.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 76 pp. Englisch.
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Taschenbuch. Zustand: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Recently,sparse signal approximation has become an increasingly important research area in signal processing. It attracts a lot of interest due to its wide range of practical applications. In this work, a novel adaptive filtering algorithm… with relative low computational complexity that is capable of exploiting the sparsity of systems is proposed. The basic idea here is, we adopt a p-norm constraint in the cost function of the variable step-size least mean square (VSSLMS) algorithm. This constrain imposes a zero attraction at each filter coefficient based on their respective relative value. Also, the convergence analysis of the proposed algorithm is presented and the stability condition is derived. The performance of the proposed algorithm has been compared to those of the Zero Attraction Least Mean Square(ZA-LMS), windowing ZA-LMS(wZA-LMS), Non-uniform Norm Constraint LMS(NNCLMS) in a system identification setting for different additive Gaussian noise(AGN), additive correlated noise(ACN)and additive impulsive noise(AIN) environments. The proposed algorithm has always shown superior performance to the others with less or comparable number of computations.
