9783838379340 - Adaptive Filters: Analyses and Applications de Salman, Mohammad; Kukrer, Osman; Hocanin, Aykut (9 resultados)

Condición: New. pp. 84.

Taschenbuch. Condición: Neu. Adaptive Filters | Analyses and Applications | Mohammad Salman (u. a.) | Taschenbuch | 84 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783838379340 | Verantwortliche Person für die EU: BoD - Books on Demand, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.

Paperback. Condición: Like New. LIKE NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this book, Mean Square Error (MSE) performance of the standard Least Mean Square (LMS), Normalized LMS (NLMS), Leaky LMS, Modified Leaky LMS (MLLMS) and Frequency Response Shaped Least Mean Square (FRS-LMS) algorithms have been investigated in Additive White Gaussian Noise (AWGN) and correlated Gaussian noise environments. The FRS-LMS algorithm has been shown to have superior performance in terms of MSE or speed of convergence compared to the other algorithms. The performance of the FRS-LMS adaptive algorithm in estimating a sinusoidal signal in impulsive and correlated noise is further studied. The algorithm does not require a priori knowledge about the nominal Gaussian process and is able to adapt to changes in the environment. The performance of the FRS-LMS is compared to that of the Leaky-LMS algorithms in terms of MSE and convergence speed. The results indicate that the FRS-LMS provides superior performance in impulsive and correlated noise environments. 84 pp. Englisch.

Condición: New. Print on Demand pp. 84 2:B&W 6 x 9 in or 229 x 152 mm Perfect Bound on Creme w/Gloss Lam.

Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Salman MohammadMohamamd Salman was born in 1977 in Palestine. He received his B.S. and M.Sc. degrees from Eastern Mediterranean University (EMU), in 2006 and 2007, respectively. He is currently pursuing his PhD at EMU. From 2006 t.

Condición: New. PRINT ON DEMAND pp. 84.

Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this book, Mean Square Error (MSE) performance of the standard Least Mean Square (LMS), Normalized LMS (NLMS), Leaky LMS, Modified Leaky LMS (MLLMS) and Frequency Response Shaped Least Mean Square (FRS-LMS) algorithms have been investigated in Additive White Gaussian Noise (AWGN) and correlated Gaussian noise environments. The FRS-LMS algorithm has been shown to have superior performance in terms of MSE or speed of convergence compared to the other algorithms. The performance of the FRS-LMS adaptive algorithm in estimating a sinusoidal signal in impulsive and correlated noise is further studied. The algorithm does not require a priori knowledge about the nominal Gaussian process and is able to adapt to changes in the environment. The performance of the FRS-LMS is compared to that of the Leaky-LMS algorithms in terms of MSE and convergence speed. The results indicate that the FRS-LMS provides superior performance in impulsive and correlated noise environments.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 84 pp. Englisch.

Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this book, Mean Square Error (MSE) performance of the standard Least Mean Square (LMS), Normalized LMS (NLMS), Leaky LMS, Modified Leaky LMS (MLLMS) and Frequency Response Shaped Least Mean Square (FRS-LMS) algorithms have been investigated in Additive White Gaussian Noise (AWGN) and correlated Gaussian noise environments. The FRS-LMS algorithm has been shown to have superior performance in terms of MSE or speed of convergence compared to the other algorithms. The performance of the FRS-LMS adaptive algorithm in estimating a sinusoidal signal in impulsive and correlated noise is further studied. The algorithm does not require a priori knowledge about the nominal Gaussian process and is able to adapt to changes in the environment. The performance of the FRS-LMS is compared to that of the Leaky-LMS algorithms in terms of MSE and convergence speed. The results indicate that the FRS-LMS provides superior performance in impulsive and correlated noise environments.