A Variable Step-Size Diffusion Normalized Least-Mean-Square Algorithm with a Combination Method Based on Mean-Square Deviation

Jung, Sang Mok; Seo, Jihye; Park, PooGyeon

doi:10.1007/s00034-015-0005-9

Cited by 38 publications

(29 citation statements)

References 17 publications

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“…Compared to other algorithms, it requires low computations thus several variations to enhance the estimation performance have been proposed. These include optimized combination coefficients [4,5], variable step size [6,7], combined optimized coefficients, and variable step size [8], and sparseness exploiting algorithms [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

z2-proportionate diffusion LMS algorithm with mean square performance analysis

2017

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Section: Introductionmentioning

confidence: 99%

z2-proportionate diffusion LMS algorithm with mean square performance analysis

2017

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“…The first algorithm to propose a variable step-size for the diffusion LMS algorithm was proposed by Saeed et al in [5] and extended by Saeed et al in [6]. Since then, several variable step-size algorithms have been proposed [7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

“…An optimal step-size for the distributed scenario was derived by *Correspondence: azzedine@kfupm.edu.sa Azzedine Zerguine is a EURASIP member 2 Department of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia Full list of author information is available at the end of the article Ghazanfari-Rad and Labeau in [9], based on which they derived a variable step-size for each iteration. A variable step-size as well as combination weights were derived using an upper bound for the mean square deviation (MSD) by Jung et al in [10]. Lee et al derived an individual step-size for each sensor by minimizing the local MSD in [11].…”

Section: Introductionmentioning

confidence: 99%

A variable step-size diffusion LMS algorithm with a quotient form

Saeed

Zerguine

2020

EURASIP J. Adv. Signal Process.

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A new variable step-size strategy for the least mean square (LMS) algorithm is presented for distributed estimation in adaptive networks using the diffusion scheme. This approach utilizes the ratio of filtered and windowed versions of the squared instantaneous error for iteratively updating the step-size. The result is that the dependence of the update on the power of the error is reduced. The performance of the algorithm improves even though it is at the cost of added computational complexity. However, the increase in computational complexity can be minimized by careful manipulation of the update equation, resulting in an excellent performance-complexity trade-off. Complete theoretical analysis is presented for the proposed algorithm including stability, transient and steady-state analyses. Extensive experimental analysis is then done to show the performance of the proposed algorithm under various scenarios. Publisher's NoteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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“…To estimate the mean-square weight deviations under the zero-mean stationary measurement noise, the proportionate-type normalized least mean square algorithms were proposed in [ 24 ]. The diffusion normalized least-mean-square algorithm (dNLMS) was proposed for parameter estimation in a distributed network [ 25 ], and the variable step size of the dNLMS algorithm was obtained by minimizing the mean-square deviation to achieve fast convergence rate. The gradient-descent total least-squares (dTLS) algorithm is a stochastic-gradient adaptive filtering algorithm that compensates for error in both input and output data [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Diffusion Logarithm-Correntropy Algorithm for Parameter Estimation in Non-Stationary Environments over Sensor Networks

Chen

Duan

et al. 2018

Sensors

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This paper considers the parameter estimation problem under non-stationary environments in sensor networks. The unknown parameter vector is considered to be a time-varying sequence. To further promote estimation performance, this paper suggests a novel diffusion logarithm-correntropy algorithm for each node in the network. Such an algorithm can adopt both the logarithm operation and correntropy criterion to the estimation error. Moreover, if the error gets larger due to the non-stationary environments, the algorithm can respond immediately by taking relatively steeper steps. Thus, the proposed algorithm achieves smaller error in time. The tracking performance of the proposed logarithm-correntropy algorithm is analyzed. Finally, experiments verify the validity of the proposed algorithmic schemes, which are compared to other recent algorithms that have been proposed for parameter estimation.

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A Variable Step-Size Diffusion Normalized Least-Mean-Square Algorithm with a Combination Method Based on Mean-Square Deviation

Cited by 38 publications

References 17 publications

z2-proportionate diffusion LMS algorithm with mean square performance analysis

z2-proportionate diffusion LMS algorithm with mean square performance analysis

A variable step-size diffusion LMS algorithm with a quotient form

Diffusion Logarithm-Correntropy Algorithm for Parameter Estimation in Non-Stationary Environments over Sensor Networks

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