Bias-compensated constrained least mean square adaptive filter algorithm for noisy input and its performance analysis

Wang, Wenyuan; Zhao, Haiquan; Lu, Lu; Yu, Yi

doi:10.1016/j.dsp.2018.07.021

Cited by 14 publications

(5 citation statements)

References 34 publications

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“…where the variable 𝜎 2 represents the same small value in Equation (5). The variable 𝜆 > 0 is a scale parameter and 𝛼 > 0is the shape parameter.…”

Section: Proposed Algorithmmentioning

confidence: 99%

“…Although conventional LMS algorithms have good performance against Gaussian noise, their convergence performance is largely affected by other noises, especially impulse noise. Various adaptive algorithms for impulse noise [3], such as the sign algorithm [4], bias-compensate algorithms [5], and the family of logarithmic cost algorithms [6,7], have been investigated to improve system robustness. Some adaptive algorithms use a step-size scaler presented by modifying the tan h cost function to exclude the effects of impulsive samples [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Affine projection Weibull M‐transform least mean square algorithm against impulsive interference

2021

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The well-known affine projection sign algorithm is one of the classic adaptive filtering methods for denoising and channel equalisation, which can achieve robust performance against coloured input and impulse noise. This work proposes the affine projection Weibull M-transform least mean square algorithm to improve the steady-state performance of affine projection algorithms. The proposed algorithm combines the Weibull M-transform cost function with the L2-norm constraint of weight vector to improve the performance of existing affine projection algorithms and achieve a better convergence rate and better misalignment. This novel algorithm also preserves good performance against impulsive interference and coloured input. Simulation results demonstrated that the proposed algorithm has a remarkable improvement in convergence performance compared with other algorithms.

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“…where the variable 𝜎 2 represents the same small value in Equation (5). The variable 𝜆 > 0 is a scale parameter and 𝛼 > 0is the shape parameter.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Affine projection Weibull M‐transform least mean square algorithm against impulsive interference

2021

View full text Add to dashboard Cite

show abstract

“…In comparison with traditional adaptive algorithm models, bias-compensated algorithms consider the influence of input and output noises simultaneously. Simulation results and performance analysis suggest that bias-compensated algorithms exhibit better performance than tra-ditional adaptive algorithms [2][3][4][5][6]. The family of the kernel adaptive algorithm demonstrates excellent performance in terms of online prediction and nonlinear problems [7,8].…”

Section: Introductionmentioning

confidence: 97%

Robust non-negative least mean square algorithm based on step-size scaler against impulsive noise

et al. 2020

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Conventional non-negative algorithms restrict the weight coefficient vector under non-negativity constraints to satisfy several inherent characteristics of a specific system. However, the presence of impulsive noise causes conventional non-negative algorithms to exhibit inferior performance. Under this background, a robust non-negative least mean square (R-NNLMS) algorithm based on a step-size scaler is proposed. The proposed algorithm uses a step-size scaler to avoid the influence of impulsive noise. For various outliers, the step-size scaler can adjust the step size of the algorithm, thereby eliminating the large error caused by impulsive noise. Furthermore, to improve the performance of the proposed algorithm in sparse system identification, the inversely-proportional R-NNLMS (IP-RNNLMS) algorithm is proposed. The simulation result demonstrates that the R-NNLMS algorithm can eliminate the influence of impulsive noise while showing fast convergence rate and low steady-state error under other noises. In addition, the IP-RNNLMS algorithm has faster convergence rate compared with the R-NNLMS algorithm under sparse system.

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“…At present, classical algorithms employed against pulse noise include the deviation compensation algorithm [18], the symbolic algorithm [19], the logarithmic cost function algorithm [20], a series of algorithms based on the generalized maximum correntropy criterion [21], and the affine projection algorithm [22], among others. In [23], the affine projection generalized maximum correntropy filtering algorithm was proposed.…”

Section: Introductionmentioning

confidence: 99%

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

Yang,

Li,

Chen

et al. 2024

Electronics

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This paper presents a machine learning-based approach to identify and separate partial discharge (PD) and two typical pulse interference (PI) signals in the vehicle-mounted cable terminations of high-speed electric multiple units (EMUs). First, a test platform was established to capture PD and two typical PI signals in these terminations. The acquired signals were then processed using the square envelope method to extract feature parameters, such as the rise time proportion, the left–right symmetry, and the upper–lower symmetry. PD signal classification was carried out on these signals, utilizing waveform parameters derived from a hierarchical clustering algorithm. The results validate that the extracted feature components effectively classify and separate PD and two typical PI signals in the vehicle-mounted cable terminations of high-speed EMUs.

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Bias-compensated constrained least mean square adaptive filter algorithm for noisy input and its performance analysis

Cited by 14 publications

References 34 publications

Affine projection Weibull M‐transform least mean square algorithm against impulsive interference

Affine projection Weibull M‐transform least mean square algorithm against impulsive interference

Robust non-negative least mean square algorithm based on step-size scaler against impulsive noise

Classification of Partial Discharge in Vehicle-Mounted Cable Termination of High-Speed Electric Multiple Unit: A Machine Learning-Based Approach

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