OCSYMLMay 19, 2018

Comments on "Momentum fractional LMS for power signal parameter estimation"

arXiv:1805.07640v15 citations
Originality Synthesis-oriented
AI Analysis

This is an incremental critique that points out errors in a recent algorithm, relevant for researchers in signal processing and adaptive filtering.

The paper identifies serious flaws in the design and analysis of the Momentum fractional LMS (mFLMS) algorithm, showing through simulations that it offers no advantage over the classical LMS method for power signal parameter estimation.

The purpose of this paper is to indicate that the recently proposed Momentum fractional least mean squares (mFLMS) algorithm has some serious flaws in its design and analysis. Our apprehensions are based on the evidence we found in the derivation and analysis in the paper titled: \textquotedblleft \textit{Momentum fractional LMS for power signal parameter estimation}\textquotedblright. In addition to the theoretical bases our claims are also verified through extensive simulation results. The experiments clearly show that the new method does not have any advantage over the classical least mean square (LMS) method.

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