The Bias-Compensated Proportionate NLMS Algorithm With Sparse Penalty Constraint

Abstract

For compensating the bias caused by the noisy input which is always ignored by ordinary algorithms, two novel algorithms with zero-attraction (ZA) penalties are proposed in this paper. The first one constructs a bias-compensated term in the updating recursion of the zero-attraction proportionate normalized least mean square (PNLMS) algorithm which is named BC-ZA-PNLMS algorithm. The second one employs the bias-compensated term and the correntropy induced metric (CIM) constraint to renew the updating recursion of the PNLMS algorithm which is named BC-CIM-PNLMS algorithm. Both of these two algorithms are derived on the basis of unbiased criterion. Simulation examples are carried out, and the results indicate that the two newly developed unbiased algorithms outperform the related algorithms previously presented in other literatures for combating noisy input and measurement noises. INDEX TERMS Unbiased adaptive filtering, zero-attraction (ZA), correntropy induced metric (CIM), PNLMS algorithm.

Robust Bias Compensation Method for Sparse Normalised Quasi-Newton Least-Mean with Variable-Mixing-Norm Adaptive Filtering

“…Yet, estimating the input noise power might be adversely affected by impulse noise present at the output of the unidentified system. The same trick that adopted CIM as the sparse penalty constraint is applied in [20], which is referred to as BC-CIM-PNLMS hereinafter. On the other hand, an L 0 norm cost function was used to accelerate the convergence for the sparse systems.…”

“…We plotted the NMSD learning curves and the evolution of mixing parameters in the simulation results by averaging over 100 independent Monte Carlo trials. The comparative works were BC-NLMS [19], BC-NLMF [21], BC-LMMN [22], BC-CIM-LGMN [23], and BC-CIM-PNLMS [20] algorithms.…”

“…Based on the unbiased criteria, several methods have been reported to compensate the biases caused by the noisy inputs, such as the bias compensation normalized least mean square algorithm (BC-NLMS) [19], bias compensation proportional normalized least mean square algorithm (BC-PNLMS) [20], bias compensation normalized least mean fourth algorithm (BC-NLMF) [21], and Disclaimer/Publisher's Note: The statements, opinions, and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions, or products referred to in the content.…”

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

“…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. This algorithm combines affine projection with generalized maximum correntropy ℓ2 for system identification in impulsive noise environments, offering improved filtering accuracy and faster convergence without computing the inverse of the input data matrix.…”