Nonparametric Variable Step-Size LMAT Algorithm

Guan, Sihai; Zhi, Li

doi:10.1007/s00034-016-0356-x

Cited by 13 publications

(16 citation statements)

References 19 publications

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“…Our previous paper's research considers a network of N sensor nodes distributed over a geographic area (as Fig. 1 ) 18 , 28 , 41 . We assume an undirected graph so that if agent n -1 is a neighbor of agent n , then agent n -1 is also a neighbor of agent n .…”

Section: Proposed the Dfair Algorithmmentioning

confidence: 99%

“…Unfortunately, they happen more in reality than we hoped. Up to now, many types the cost functions have been used to design adaptive filtering algorithms, such as least mean absoult third 18 , least mean fourth 19 , entropy 20 , least-squares estimator 21 , absolute value estimator 22 , the Cauchy 23 , Geman–McClure 24 , Welsch 25 , 26 , and Huber function 27 – 30 . Specifically, the least-squares estimator is not robust because their influence function is not bounded 21 , and the absolute value estimator is not stable because the function of estimate error ( ) at i -th is not strictly convex 22 .…”

Section: Introductionmentioning

confidence: 99%

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Diffusion adaptive filtering algorithm based on the Fair cost function

Guan

Cheng²,

Zhao

et al. 2021

Sci Rep

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To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.

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Section: Proposed the Dfair Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Diffusion adaptive filtering algorithm based on the Fair cost function

Guan

Cheng²,

Zhao

et al. 2021

Sci Rep

Self Cite

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“…with f 1 (e(i)) = e 2 (i)sign[e(i)] 1+β|e(i)| 3 . Premultiplying both sides of (24) by their transposes, using (22), and taking the expected value, we obtain E w(i + 1) 2 = E w(i) 2 − 2µE [e a (i)f 1 (e(i))]…”

Section: A Performance Of Rnlmatmentioning

confidence: 99%

“…A nonparametric variable step-size least mean absolute third (NVSLMAT) algorithm is proposed to improve the capability of LMAT against non-Gaussian noises [22]. Further, to combat Gaussian and non-Gaussian noises in the timevarying unknown system under low signal-to-noise ratio, an optimized least mean absolute third (OPLMAT) algorithm is proposed in [23].…”

Section: Introductionmentioning

confidence: 99%

Robust Normalized Least Mean Absolute Third Algorithms

2019

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This paper addresses the stability issues of the least mean absolute third (LMAT) algorithm using the normalization based on the third order in the estimation error. A novel robust normalized least mean absolute third (RNLMAT) algorithm is therefore proposed to be stable for all statistics of the input, noise, and initial weights. For further improving the filtering performance of RNLMAT in different noises and initial conditions, the variable step-size RNLMAT (VSSRNLMAT) and the switching RNLMAT (SWRNLMAT) algorithms are proposed using the statistics of the estimation error and a switching method, respectively. The filtering performance of RNLMAT is improved by VSSRNLMAT and SWRNLMAT at the expense of affordable computational cost. RNLMAT with less computational complexity than other normalized adaptive filtering algorithms, can provide better filtering accuracy and robustness against impulsive noises. The steady-state performance of RNLMAT and SWRNLMAT in terms of the excess mean-square error is performed for theoretical analysis. Simulations conducted in system identification under different noise environments confirm the theoretical results and the superiorities of the proposed algorithms from the aspects of filtering accuracy and robustness against large outliers.

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“…These techniques of varying the step‐size depend on predefined parameters and parameters being updated with respect to time. So to prevent the algorithm's dependence on parameters, nonparametric variable step‐size (NPVSS) methods were introduced such as the NPVSS normalized LMS (VSS‐NLMS) algorithm 21 and the NPVSS LMAT algorithm 22 whose step‐sizes were dependent on the immediate value of the posterior error estimate and present error estimate.…”

Section: Introductionmentioning

confidence: 99%

The variable step‐sizeLMS/F algorithm using nonparametric method for adaptive system identification

Patnaik

Nanda

2020

Adaptive Control & Signal

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A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real-time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step-size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step-size equation, this article proposes the nonparametric variable step-size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real-time setup validate that the proposed NPVSS-LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise.

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Nonparametric Variable Step-Size LMAT Algorithm

Cited by 13 publications

References 19 publications

Diffusion adaptive filtering algorithm based on the Fair cost function

Diffusion adaptive filtering algorithm based on the Fair cost function

Robust Normalized Least Mean Absolute Third Algorithms

The variable step‐sizeLMS/F algorithm using nonparametric method for adaptive system identification

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