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RVP-FLMS : A Robust Variable Power Fractional LMS Algorithm

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Jawwad Ahmad, Muhammad Usman, Shujaat Khan, Imran Naseem, Hassan Jamil Syed

Optimization and Control · Submitted 20 Jan 2017

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS.

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arXiv ID1701.05677 (version 1)
Pith IDZQ24S73X, Pith's permanent record of this paper and its review
CitationHassan Jamil Syed, Imran Naseem, Jawwad Ahmad, Muhammad Usman, Shujaat Khan. "RVP-FLMS : A Robust Variable Power Fractional LMS Algorithm." arXiv:1701.05677 (2017). https://pith.science/paper/ZQ24S73X
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,183 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.05 · human participants 0.03 · controlled comparison 0.90 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.95 · mentions code or data availability 0.98.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problems of system identification and channel equalization are considered. The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS. The MATLAB code for the related simulation is available online at https://goo.gl/dGTGmP.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. contribution 1.00 0.88 0.05 asserted 0.96
s1 The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. contribution 0.68 0.78 0.29 asserted 1.00
s2 For the evaluation purpose, the problems of system identification and channel equalization are considered. method 0.92 0.45 0.02 asserted 0.81
s3 The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS. result 1.00 0.95 0.86 asserted 1.00
s4 The MATLAB code for the related simulation is available online at https://goo.gl/dGTGmP. contribution 0.59 0.35 0.02 asserted 0.99

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from simulation or computation 0.97.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

In this paper, we propose an adaptive framework for the variable power of the fractional least mean square (FLMS) algorithm. The proposed algorithm named as robust variable power FLMS (RVP-FLMS) dynamically adapts the fractional power of the FLMS to achieve high convergence rate with low steady state error. For the evaluation purpose, the problems of system identification and channel equalization are considered. The experiments clearly show that the proposed approach achieves better convergence rate and lower steady-state error compared to the FLMS. The MATLAB code for the related simulation is available online at https://goo.gl/dGTGmP.

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