Evidence map›Paper›PMID 40285255›Full record

ArticleSensors (Basel, Switzerland)2025

A Variable Step-Size FxLMS Algorithm for Nonlinear Feedforward Active Noise Control.

Thi Trung Tin Nguyen, Faxiang Zhang, Jing Na, Le Thai Nguyen, Gengen Li, Altyib Abdallah Mahmoud Ahmed

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Thi Trung Tin NguyenYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.
Faxiang ZhangYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.ORCID 0000-0002-2983-5728
Jing NaYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.ORCID 0000-0002-3067-1580
Le Thai NguyenFaculty of Engineering and Technology, Nguyen Tat Thanh University, Ho Chi Minh City 700000, Vietnam.
Gengen LiYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.ORCID 0009-0004-7993-0477
Altyib Abdallah Mahmoud AhmedYunnan Key Laboratory of Intelligent Control and Application, Faculty of Mechanical & Electrical Engineering, Kunming University of Science & Technology, Kunming 650500, China.

Funding

National Natural Science Foundation of China 62461160260, 62303200, 62433012, 62273169project of the Yunnan Province's "Xingdian Talents Support Plan" KKXX202401036Yunnan Fundamental Research Projects 202401CF070163, 202301BE070001-064
6 · The paper itself

Abstract

Active noise control (ANC) represents an efficient technology for enhancing the noise suppression performance and ensuring the stable operation of multi-sensor systems through generative model-enhanced data representation and dynamic information fusion across heterogeneous sensors due to the complexity of the real-world environment. To address problems caused by a nonlinear noise source, a novel adaptive neuro-fuzzy network controller is proposed for feedforward nonlinear ANC systems based on a variable step-size filtered-x least-mean-square (VSS-LMS) algorithm. Specifically, the LMS algorithm is first introduced to update the weight parameters of the controller based on the adaptive neuro-fuzzy network. Then, a variable step-size adjustment strategy is proposed to calculate the learning gain used in the LMS algorithm, which aims to improve the nonlinear noise suppression performance. Additionally, the stability of the proposed method is proven by the discrete Lyapunov theorem. Extensive simulation experiments show that the proposed method surpasses the mainstream ANC methods with regard to nonlinear noise.

Indexed as

active noise controladaptive neuro-fuzzy networkfiltered-x least-mean-square algorithmnonlinear pathvariable step-size learning

Identifiers

PMID40285255
PMCPMC12031338

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.