Sparse leaky‐LMS algorithm for system identification and its convergence analysis

Salman, Mohammad Shukri

doi:10.1002/acs.2428

Cited by 27 publications

(21 citation statements)

References 13 publications

(9 reference statements)

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“…In the third experiment, we demonstrate the performance when the input signal is a fragment of 2 s of real speech, sampled at 8kHZ [4,8]. Figure 9 shows an acoustic echo path of a 1024-tap system with 52 non-zero coefficients, which can be considered to be very sparse and is used in the simulation.…”

Section: Methodsmentioning

confidence: 99%

“…In general, a sparse adaptive filtering algorithm can be derived by incorporating a sparsity penalty term (SPT), such as the l0-norm, into a traditional adaptive algorithm. Typical examples of sparse adaptive filtering algorithms include sparse least mean square (LMS) [1][2][3][4], sparse affine projection algorithms (APA) [5], sparse recursive least squares (RLS) [6], and their variations [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

Peng

et al. 2015

Entropy

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Abstract:Recently, sparse adaptive learning algorithms have been developed to exploit system sparsity as well as to mitigate various noise disturbances in many applications. In particular, in sparse channel estimation, the parameter vector with sparsity characteristic can be well estimated from noisy measurements through a sparse adaptive filter. In previous studies, most works use the mean square error (MSE) based cost to develop sparse filters, which is rational under the assumption of Gaussian distributions. However, Gaussian assumption does not always hold in real-world environments. To address this issue, we incorporate in this work an l1-norm or a reweighted l1-norm into the minimum error entropy (MEE) criterion to develop new sparse adaptive filters, which may perform much better than the MSE based methods, especially in heavy-tailed non-Gaussian situations, since the error entropy can capture higher-order statistics of the errors. In addition, a new approximator of l0-norm, based on the correntropy induced metric (CIM), is also used as a sparsity penalty term (SPT). We analyze the mean square convergence of the proposed new sparse adaptive filters. An energy conservation relation is derived and a sufficient condition is obtained, which ensures the mean square convergence. Simulation results confirm the superior performance of the new algorithms. OPEN ACCESSEntropy 2015, 17 3420 Keywords: sparse estimation; minimum error entropy; correntropy induced metric; mean square convergence; impulsive noise MSC Codes: 62B10

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

Peng

et al. 2015

Entropy

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“…Finally, we set up an experiment to study the tracking behavior of our GZA-PNMCC algorithm for estimating a long-tap echo channel with two different sparsity levels and a length of 256. The sparsity measurement of the echo channel is ζ 12 [44][45][46][47][48]. A typical echo channel is described in Figure 6.…”

Section: Behavior Of the Proposed Gza-pnmcc Algorithmmentioning

confidence: 99%

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

Wang

Albu

et al. 2017

Symmetry

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Abstract:A general zero attraction (GZA) proportionate normalized maximum correntropy criterion (GZA-PNMCC) algorithm is devised and presented on the basis of the proportionate-type adaptive filter techniques and zero attracting theory to highly improve the sparse system estimation behavior of the classical MCC algorithm within the framework of the sparse system identifications. The newly-developed GZA-PNMCC algorithm is carried out by introducing a parameter adjusting function into the cost function of the typical proportionate normalized maximum correntropy criterion (PNMCC) to create a zero attraction term. The developed optimization framework unifies the derivation of the zero attraction-based PNMCC algorithms. The developed GZA-PNMCC algorithm further exploits the impulsive response sparsity in comparison with the proportionate-type-based NMCC algorithm due to the GZA zero attraction. The superior performance of the GZA-PNMCC algorithm for estimating a sparse system in a non-Gaussian noise environment is proven by simulations.

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“…The ZA algorithms cannot distinguish zero taps and nonzero taps, which exert ZA on all the filter coefficients and hence may degrade their performance. After that, zero-attracting techniques have been introduced into the proportionate adaptive filter algorithms [5,16,18], leaky least mean square [19] and normalized least mean square algorithms [7] to form desired zero attractors [6,11,17]. Their convergence characteristics are analyzed in [10,22].…”

Section: Introductionmentioning

confidence: 99%

Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

Zhang

Wang

2015

Circuits Syst Signal Process

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In this paper, an improved sparse-aware affine projection (AP) algorithm for sparse system identification is proposed and investigated. The proposed sparse AP algorithm is realized by integrating a non-uniform norm constraint into the cost function of the conventional AP algorithm, which can provide a zero attracting on the filter coefficients according to the value of each filter coefficient. Low complexity is obtained by using a linear function instead of the reweighting term in the modified AP algorithm to further improve the performance of the proposed sparse AP algorithm. The simulation results demonstrate that the proposed sparse AP algorithm outperforms the conventional AP and previously reported sparse-aware AP algorithms in terms of both convergence speed and steady-state error when the system is sparse.

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Sparse leaky‐LMS algorithm for system identification and its convergence analysis

Cited by 27 publications

References 13 publications

Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

A General Zero Attraction Proportionate Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification

Low-Complexity Non-Uniform Penalized Affine Projection Algorithm for Sparse System Identification

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