9783846548028 - Adaptive Filtering: Fundamentals and Applications de Shukri Salman, Mohammad; Kukrer, Osman; Hocanin, Aykut (7 resultados)

Paperback. Condición: Brand New. 104 pages. 8.66x5.91x0.24 inches. In Stock.

Taschenbuch. Condición: Neu. Adaptive Filtering | Fundamentals and Applications | Mohammad Shukri Salman (u. a.) | Taschenbuch | 104 S. | Englisch | 2011 | LAP LAMBERT Academic Publishing | EAN 9783846548028 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

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Taschenbuch. Condición: 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.

Condición: 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.

Taschenbuch. Condición: 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 Landstraße 99, 66123 Saarbrücken 104 pp. Englisch.

Taschenbuch. Condición: 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.