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Error Gradient-based Variable-Lp Norm Constraint LMS Algorithm for Sparse System Identification [article]

Yong Feng, Fei Chen, Rui Zeng, Jiasong Wu, Huazhong Shu
2015 arXiv   pre-print
To address this problem, we propose a novel variable p-norm constraint least mean square (LMS) algorithm, which serves as a variant of the conventional Lp-LMS algorithm established for sparse system identification  ...  However, when applied for system identification, most priori work in sparse norm constraint adaptive filtering suffers from the difficulty of adaptability to the sparsity of the systems to be identified  ...  of sparse norm constraint adaptive filtering suffer from the difficulty of adaptability to the sparsity of system, the present work in this paper develops a novel Lp norm constraint LMS algorithm coined  ... 
arXiv:1509.07951v1 fatcat:3rvoarhci5gdrbay3nx3p7dxuq

Variable p norm constrained LMS algorithm based on gradient of root relative deviation.pdf [article]

Yong Feng, Fei Chen, Jiasong Wu
2016 arXiv   pre-print
A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy of varying p is presented, which is applied to system identification in this letter.  ...  of sparse system identification in the presence of noise.  ...  Variable p norm constrained LMS algorithm based on gradient of root relative deviation Yong Feng ✉ , Fei Chen and Jiasong Wu A new Lp-norm constraint least mean square (Lp-LMS) algorithm with new strategy  ... 
arXiv:1603.09022v1 fatcat:wqq3gnnukvabzgfttnxnuot5ju

Sparsity Aware Normalized Least Mean p-power Algorithms with Correntropy Induced Metric Penalty [article]

Wentao Ma, Hua Qu, Jihong Zhao, Badong Chen, Guan Gui
2015 arXiv   pre-print
Based on the first proposed algorithm, moreover, we propose an improved CIM constraint variable regularized NLMP(CIMVRNLMP) algorithm by utilizing variable regularized parameter(VRP) selection method which  ...  To exploit sparsity as well as to mitigate the impulsive noise, this paper proposes a sparse NLMP algorithm, i.e., Correntropy Induced Metric (CIM) constraint based NLMP (CIMNLMP).  ...  least square (RLS) [2] , -norm constrained LMS (L0-LMS) [3] , -norm constrained LMS(LP-LMS) [4] and its variations [5] [6] [7] ) have been developed and also applied successfully in many applications  ... 
arXiv:1503.00792v1 fatcat:xqb3un76lbdvvf5u5vx52eszb4

A Sparsity-Aware Variable Kernel Width Proportionate Affine Projection Algorithm for Identifying Sparse Systems

Zhengxiong Jiang, Yingsong Li, Xinqi Huang, Zhan Jin
2019 Symmetry  
A sparsity-aware variable kernel width proportionate affine projection  ...  The developed LP-VPAP is realized by developing the variable kernel width technique and the l p -norm-like constraint.  ...  The Developed LP-VPAP Algorithm Herein, we detailedly analyze the developed LP-VPAP algorithm, which is implemented by the variable kernel width technique and the l p -norm-like constraint to devise a  ... 
doi:10.3390/sym11101218 fatcat:vbw5wqqpf5exnbriynsqdnauqa

A Class of Diffusion Zero Attracting Stochastic Gradient Algorithms with Exponentiated Error Cost Functions

Zhengyan Luo, Haiquan Zhao, Xiangping Zeng
2019 IEEE Access  
For sparse system identification in the adaptive network, a polynomial variable scaling factor improved diffusion least sum of exponentials (PZA-VSIDLSE) algorithm and an l p -norm constraint diffusion  ...  Distributed estimation algorithms based on the popular mean-square error criterion have poor behavior for sparse system identification with color noise.  ...  Simulations are implemented to verify the performances of the LP-DLE2 and PZA-VSIDLSE algorithms for the sparse system identification.  ... 
doi:10.1109/access.2019.2961162 fatcat:dzruc2mnavcvji6c2bfs2tnpga

Sparsity aware normalized least mean p-power algorithms with correntropy induced metric penalty

Wentao Ma, Hua Qu, Jihong Zhao, Badong Chen, Guan Gui
2015 2015 IEEE International Conference on Digital Signal Processing (DSP)  
The second one is an improved CIM constraint variable regularized NLMP (CIMVRNLMP) algorithm, in which variable regularized parameter (VRP) is selected to adjust convergence speed and steady-state error  ...  However, the standard algorithm is developed without considering the inherent sparse structure distribution of unknown system.  ...  ) [1] , sparse regularized least square (RLS) [2] , 0 l -norm constrained LMS (L0-LMS) [3] , p l -norm constrained LMS(LP-LMS) [4] and its variations [5] [6] [7] ) have been developed and also applied  ... 
doi:10.1109/icdsp.2015.7251952 dblp:conf/icdsp/MaQZCG15 fatcat:xy66sdebqbe5ven4mhoejgvxee

Two Are Better Than One: Adaptive Sparse System Identification Using Affine Combination of Two Sparse Adaptive Filters

Guan Gui, Shinya Kumagai, Abolfazl Mehbodniya, Fumiyuki Adachi
2014 2014 IEEE 79th Vehicular Technology Conference (VTC Spring)  
One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter.  ...  Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming.  ...  Koichi Adachi of Institute for Infocomm Research for his valuable comments and suggestions.  ... 
doi:10.1109/vtcspring.2014.7023132 dblp:conf/vtc/GuiKMA14 fatcat:qdh5kzlx6zdntid7jyrhutco3y

Two are Better Than One: Adaptive Sparse System Identification using Affine Combination of Two Sparse Adaptive Filters [article]

Guan Gui, Shinya Kumagai, Abolfazl Mehbodniya, Fumiyuki Adachi
2013 arXiv   pre-print
One of popular adaptive sparse system identification (ASSI) methods is adopting only one sparse least mean square (LMS) filter.  ...  Sparse system identification problems often exist in many applications, such as echo interference cancellation, sparse channel estimation, and adaptive beamforming.  ...  ACKNOWLEDGMENT This work was supported by grant-in-aid for the Japan Society for the Pro motion of Science (JSPS) fellows grant number 24 02366.  ... 
arXiv:1311.1312v1 fatcat:ferj4ofzxzhuxn3f7s3luapulq

Underwater Acoustic Channel Adaptive Estimation using l21 Norms [article]

Amer Aljanabi, Mohanad Abd Shehab, Osama Alluhaibi, Qasim Zeeshan Ahmed, Pavlos Lazaridis
2021 arXiv   pre-print
The problem of underwater acoustic (UWA) channel estimation is the non-uniform sparse representation that may increase the algorithm complexity and the required time.  ...  Furthermore, it can achieve a better performance in terms of mean square error (MSE) and execution time.  ...  Mei, "l f0g norm constraint lms algorithm for sparse system identification," IEEE Signal Processing Letters, vol. 16, no. 9, pp. 774-777, 2009 . References 1 - 1 A. S. Gupta and G. R.  ... 
arXiv:2108.13189v1 fatcat:ygokuty4qvfyfgcf3umvrbzowu

An Improved Proportionate Normalized Least-Mean-Square Algorithm for Broadband Multipath Channel Estimation

Yingsong Li, Masanori Hamamura
2014 The Scientific World Journal  
Our simulation results demonstrate that the proposed algorithm can effectively improve the estimation performance of the PNLMS-based algorithm for sparse channel estimation applications.  ...  To make use of the sparsity property of broadband multipath wireless communication channels, we mathematically propose anlp-norm-constrained proportionate normalized least-mean-square (LP-PNLMS) sparse  ...  echo cancellation and system identification, which are known as the zero-attracting LMS (ZA-LMS) and reweighted ZA-LMS (RZA-LMS) algorithms, respectively [15] .  ... 
doi:10.1155/2014/572969 pmid:24782663 pmcid:PMC3981014 fatcat:zc3udzyljfe33la6zp6fri7s24

A Stochastic Gradient Approach on Compressive Sensing Signal Reconstruction Based on Adaptive Filtering Framework

Jian Jin, Yuantao Gu, Shunliang Mei
2010 IEEE Journal on Selected Topics in Signal Processing  
This approach employs a stochastic gradient-based adaptive filtering framework, which is commonly used in system identification, to solve the sparse signal reconstruction problem.  ...  Based on the methodological similarity between sparse signal reconstruction and system identification, a new approach for sparse signal reconstruction in compressive sensing (CS) is proposed in this paper  ...  Detao Mao at the University of British Columbia for his part in improving the English expression of this paper.  ... 
doi:10.1109/jstsp.2009.2039173 fatcat:6kfjlois6vdgfokyk3edbr6iri

Reweighted l1-norm Penalized LMS for Sparse Channel Estimation and Its Analysis [article]

Omid Taheri, Sergiy A. Vorobyov
2014 arXiv   pre-print
A new reweighted l1-norm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper.  ...  An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted l1-norm penalized LMS algorithm outperforms the standard  ...  In [13] , the idea of using a weighted l 1 -norm penalty for the purpose of sparse system identification is presented without any convergence analysis.  ... 
arXiv:1401.3566v1 fatcat:yijvimxbf5haxf2t2gnvhudefm

Reweighted l1-norm penalized LMS for sparse channel estimation and its analysis

Omid Taheri, Sergiy A. Vorobyov
2014 Signal Processing  
A new reweighted l 1 Ànorm penalized least mean square (LMS) algorithm for sparse channel estimation is proposed and studied in this paper.  ...  An expression for the excess mean square error (MSE) of the algorithm is also derived which suggests that under the right conditions, the reweighted l 1 Ànorm penalized LMS algorithm outperforms the standard  ...  In [13] , the idea of using a weighted l 1 Ànorm penalty for the purpose of sparse system identification is presented without any convergence analysis.  ... 
doi:10.1016/j.sigpro.2014.03.048 fatcat:s4iledbvifhxvb3u3nsfc2tbcy

Efficient Parameters Estimation Method for the Separable Nonlinear Least Squares Problem

Ke Wang, Guolin Liu, Qiuxiang Tao, Min Zhai
2020 Complexity  
In this work, we combine the special structure of the separable nonlinear least squares problem with a variable projection algorithm based on singular value decomposition to separate linear and nonlinear  ...  Then, we propose finding the nonlinear parameters using the Levenberg–Marquart (LM) algorithm and either solve the linear parameters using the least squares method directly or by using an iteration method  ...  Solution of Parameters for Separable Nonlinear Least Squares Variable Projection Algorithm Based on SVD.  ... 
doi:10.1155/2020/9619427 fatcat:ychefgh5lbcydk64svmgllak7u

A Survey on Nonconvex Regularization Based Sparse and Low-Rank Recovery in Signal Processing, Statistics, and Machine Learning [article]

Fei Wen, Lei Chu, Peilin Liu, Robert C. Qiu
2019 arXiv   pre-print
algorithms.  ...  This paper gives an overview of this topic in various fields in signal processing, statistics and machine learning, including compressive sensing (CS), sparse regression and variable selection, sparse  ...  Least-Mean-Square (LMS) Filter: For sparse system identification, regularized least-mean-square (LMS) algorithms have shown advantage over traditional LMS algorithms, e.g., be more accurate, more efficient  ... 
arXiv:1808.05403v3 fatcat:lfq3t5gvgngmllu27ml7xnehtm
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