A $$p$$ p -norm variable step-size LMS algorithm for sparse system identification

Aliyu, Muhammad L.; Alkassim, Mujahid A.; Salman, Mohammad Shukri

doi:10.1007/s11760-013-0610-7

Cited by 34 publications

(30 citation statements)

References 17 publications

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“…In the first three sparse systems, there are 32 coefficients in the sparse system and the number of the nonzero taps K are 1, 4 and 8 for the first, second and third experiments, respectively. In these three experiments, the positions of the nonzero taps are distributed randomly within the length of the sparse channel and these nonzero taps are set to 1, which is similar to [1,2,8,13,15,24]. Both the driven input signal and the system independent additive noise are assumed to be white Gaussian distribution with zero mean and variances of 1 and 0.001, respectively.…”

Section: Resultsmentioning

confidence: 99%

“…Similar to l 0 -LMS, l 1 -LMS, l 0 -AP and l 1 -AP algorithms [8,12,13,15,24], the matrix P can assign different value p i to different entries ofŵ(n), and hence, the last two terms of the right hand of Eq. (17) can classify the filter coefficients into small and large groups depending on the absolute value ofŵ i (n) [1,24]. Since we are interested in the minimum value P, the minimization of the last two terms at ith iteration…”

Section: Proposed Sparse Ap Algorithmsmentioning

confidence: 99%

“…Thus, the proposed NNP-AP algorithm can improve the estimation performance. From the concepts of the reweighted techniques [1,2,15], the estimation performance of the proposed NNP-AP algorithm can be further improved by the introduction of the reweighting technique into the Eq. (20) to produce a reweighted zero attractor.…”

Section: Proposed Sparse Ap Algorithmsmentioning

confidence: 99%

“…However, the NNCLMS algorithm has a fixed step-size, which will reduce the performance when it is used to track the quick time-varying systems. Subsequently, a variable step-size (VSS) technique has been introduced into the NNCLMS algorithm [1], which improves the convergence speed and state-state performance. However, these algorithms were proposed based on the LMS algorithm which performed poorly when the input signal was correlated.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Resultsmentioning

confidence: 99%

Section: Proposed Sparse Ap Algorithmsmentioning

confidence: 99%

Section: Proposed Sparse Ap Algorithmsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Zhang

Wang

2015

Circuits Syst Signal Process

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“…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%

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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Intelligent Solar Grid Integration: Advancements in Control Strategies and Power Quality Enhancement

Shambhu Choudhary,

Nath Gupta,

Hussain

2024

Circuit Theory & Apps

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This study delves into the advancements, challenges, and opportunities in the solar grid technology, emphasizing its integration into the existing power infrastructure. The proposed ML‐FOGI (multilayer fourth‐order generalized integrator) algorithm presents a promising solution for modern grid synchronization requirements, addressing challenges posed by the integration of renewable energy into the distributed generation. This technology enhances system robustness and dynamic response, particularly in weak distribution grids characterized by voltage distortions and imbalances. This innovative approach enhances system stability and performance, particularly in polluted grid conditions. Through MATLAB/Simulink modelling and experimental validation, the effectiveness of the proposed control strategy is investigated across diverse testing conditions. Emphasizing the significant role of the control strategy in enhancing power quality and grid stability in the solar photovoltaic systems, this research underscores the importance of robust and adaptive control mechanisms for optimizing performance and ensuring grid reliability in modern microgrid. This study delves into this complex issue of working of the grid connected photovoltaic (GCPV) system, emphasizing the importance of balanced unit templates and a stable DC link voltage with unpredictable grid harmonics. This research aims to enhance the resilience of solar PV systems against grid disturbances by drawing inspiration from the adaptable nature of ADALINE filters and adhering to the IEEE519 requirements. This study employs OPAL‐RT's resilient experimental configuration.

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A $$p$$ p -norm variable step-size LMS algorithm for sparse system identification

Cited by 34 publications

References 17 publications

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

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

Minimum Error Entropy Algorithms with Sparsity Penalty Constraints

Intelligent Solar Grid Integration: Advancements in Control Strategies and Power Quality Enhancement

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