Diffusion adaptive filtering algorithm based on the Fair cost function

Diffusion adaptive filtering algorithm based on the Fair cost function

Abstract To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) a...

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Main Authors: Sihai Guan, Qing Cheng, Yong Zhao, Bharat Biswal
Format: Article
Language:English
Published: Nature Publishing Group 2021-10-01
Series:Scientific Reports
Online Access:https://doi.org/10.1038/s41598-021-99330-9
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spelling doaj-84dd53a3b17644309aba799350e6cebc2021-10-10T11:30:21ZengNature Publishing GroupScientific Reports2045-23222021-10-0111111310.1038/s41598-021-99330-9Diffusion adaptive filtering algorithm based on the Fair cost functionSihai Guan0Qing Cheng1Yong Zhao2Bharat Biswal3College of Electronic and Information, Southwest Minzu UniversitySichuan Vocational College of Finance and EconomicsSchool of Mechanical and Power Engineering, Henan Polytechnic UniversityThe Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of ChinaAbstract To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.https://doi.org/10.1038/s41598-021-99330-9
collection DOAJ
language English
format Article
sources DOAJ
author Sihai Guan
Qing Cheng
Yong Zhao
Bharat Biswal
spellingShingle Sihai Guan
Qing Cheng
Yong Zhao
Bharat Biswal
Diffusion adaptive filtering algorithm based on the Fair cost function
Scientific Reports
author_facet Sihai Guan
Qing Cheng
Yong Zhao
Bharat Biswal
author_sort Sihai Guan
title Diffusion adaptive filtering algorithm based on the Fair cost function
title_short Diffusion adaptive filtering algorithm based on the Fair cost function
title_full Diffusion adaptive filtering algorithm based on the Fair cost function
title_fullStr Diffusion adaptive filtering algorithm based on the Fair cost function
title_full_unstemmed Diffusion adaptive filtering algorithm based on the Fair cost function
title_sort diffusion adaptive filtering algorithm based on the fair cost function
publisher Nature Publishing Group
series Scientific Reports
issn 2045-2322
publishDate 2021-10-01
description Abstract To better perform distributed estimation, this paper, by combining the Fair cost function and adapt-then-combine scheme at all distributed network nodes, a novel diffusion adaptive estimation algorithm is proposed from an M-estimator perspective, which is called the diffusion Fair (DFair) adaptive filtering algorithm. The stability of the mean estimation error and the computational complexity of the DFair are theoretically analyzed. Compared with the robust diffusion LMS (RDLMS), diffusion Normalized Least Mean M-estimate (DNLMM), diffusion generalized correntropy logarithmic difference (DGCLD), and diffusion probabilistic least mean square (DPLMS) algorithms, the simulation experiment results show that the DFair algorithm is more robust to input signals and impulsive interference. In conclusion, Theoretical analysis and simulation results show that the DFair algorithm performs better when estimating an unknown linear system in the changeable impulsive interference environments.
url https://doi.org/10.1038/s41598-021-99330-9
work_keys_str_mv AT sihaiguan diffusionadaptivefilteringalgorithmbasedonthefaircostfunction
AT qingcheng diffusionadaptivefilteringalgorithmbasedonthefaircostfunction
AT yongzhao diffusionadaptivefilteringalgorithmbasedonthefaircostfunction
AT bharatbiswal diffusionadaptivefilteringalgorithmbasedonthefaircostfunction
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