A Robust Variable Step Size Fractional Least Mean Square (RVSS-FLMS) Algorithm

TL;DR

This paper introduces the RVSS-FLMS algorithm, an adaptive method that dynamically adjusts the step size in fractional LMS to improve convergence speed and reduce steady state error in system identification tasks.

Contribution

The paper presents a novel robust variable step size framework for FLMS, enhancing convergence rate and accuracy over existing FLMS variants.

Findings

Achieves faster convergence than FLMS and AMFLMS.

Reduces steady state error in system identification.

Demonstrates robustness across different system models.

Abstract

In this paper, we propose an adaptive framework for the variable step size of the fractional least mean square (FLMS) algorithm. The proposed algorithm named the robust variable step size-FLMS (RVSS-FLMS), dynamically updates the step size of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problem of system identification is considered. The experiments clearly show that the proposed approach achieves better convergence rate compared to the FLMS and adaptive step-size modified FLMS (AMFLMS).