Abstract—In this paper, a novel weighted zero-attracting leaky-LMS (WZA-LLMS) adaptive algorithm for sparse systems is proposed. In the proposed algorithm, a log-sum penalty is incor-porated into the cost function of the leaky-LMS algorithm, which results in a shrinkage in the update equation. This shrinkage gives the algorithm the ability of attracting zeros, i.e., when the system is sparse, and hence improves its performance. The performance of the proposed WZA-LLMS algorithm is compared to those of the standard leaky-LMS and ZA-LMS algorithms in sparse system identification settings. The WZA-LLMS algorithm shows superior performance compared to the algorithms. I
Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advant...
This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a...
Sparse system identification has attracted much attention in the field of adaptive algorithms, and t...
In this paper, we develop the adaptive algorithm for system identification where the model is sparse...
This paper presents a novel adaptive algorithm based on RZA-LMS for sparse signal and system identif...
This paper proposes a new approach to identify time varying sparse systems. The proposed approach us...
Abstract—In order to improve the performance of Least Mean Square (LMS) based system identification ...
International audienceZero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for ...
International audienceZero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for ...
For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NL...
Adaptive filters are extensively used in the identification of an unknown system. Unlike several gra...
We present analytical results, and details of implementation for a novel adaptive filter incorporati...
In this thesis, low-complexity adaptive filtering algorithms that exploit the sparsity of signals an...
Sparse systems are those systems, the impulse response of which contains a signi_cant number of zero...
In this thesis, low-complexity adaptive filtering algorithms that exploit the sparsity of signals an...
Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advant...
This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a...
Sparse system identification has attracted much attention in the field of adaptive algorithms, and t...
In this paper, we develop the adaptive algorithm for system identification where the model is sparse...
This paper presents a novel adaptive algorithm based on RZA-LMS for sparse signal and system identif...
This paper proposes a new approach to identify time varying sparse systems. The proposed approach us...
Abstract—In order to improve the performance of Least Mean Square (LMS) based system identification ...
International audienceZero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for ...
International audienceZero-attracting least-mean-square (ZA-LMS) algorithm has been widely used for ...
For the sparse system estimation problem, the l0 norm constraint normalized least mean square (l0-NL...
Adaptive filters are extensively used in the identification of an unknown system. Unlike several gra...
We present analytical results, and details of implementation for a novel adaptive filter incorporati...
In this thesis, low-complexity adaptive filtering algorithms that exploit the sparsity of signals an...
Sparse systems are those systems, the impulse response of which contains a signi_cant number of zero...
In this thesis, low-complexity adaptive filtering algorithms that exploit the sparsity of signals an...
Standard least mean square/fourth (LMS/F) is a classical adaptive algorithm that combined the advant...
This paper provides a novel normalized sign least-mean square (NSLMS) algorithm which updates only a...
Sparse system identification has attracted much attention in the field of adaptive algorithms, and t...