Adaptive Filtering: Fundamentals and Applications

 
9783846548028: Adaptive Filtering: Fundamentals and Applications
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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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About the Author:

M. 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 SCI & SCI exp journals and he is a TPC member of many conferences. He is currently an Assist. Prof. Dr. in EEE dept at Mevlana University.

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Book Description Condition: New. Publisher/Verlag: LAP Lambert Academic Publishing | Fundamentals and Applications | 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. | Format: Paperback | Language/Sprache: english | 104 pp. Seller Inventory # K9783846548028

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Book Description LAP Lambert Academic Publishing Nov 2011, 2011. Taschenbuch. Condition: Neu. 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. Seller Inventory # 9783846548028

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Book Description LAP Lambert Academic Publishing Nov 2011, 2011. Taschenbuch. Condition: Neu. 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. Seller Inventory # 9783846548028

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Book Description LAP Lambert Academic Publishing, 2011. Paperback. Condition: New. Aufl.. Language: English . Brand New Book. 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. Seller Inventory # KNV9783846548028

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Book Description LAP Lambert Academic Publishing Nov 2011, 2011. Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand 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. Seller Inventory # 9783846548028

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