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Diffusion Combinatoric Correntropy Algorithm for Distributed Estimation.

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Title: Diffusion Combinatoric Correntropy Algorithm for Distributed Estimation.
Authors: Wang, Shengwei1 (AUTHOR) wangsw@nwnu.edu.cn, Xu, Yurong1 (AUTHOR) 2855463192@qq.com, Xu, Tianci2 (AUTHOR) 1620207634@qq.com, Yang, Kuojian2 (AUTHOR) 532152961@qq.com
Source: Circuits, Systems & Signal Processing. Feb2025, Vol. 44 Issue 2, p889-910. 22p.
Subjects: Cost functions, Burst noise, Distributed algorithms, System identification, Artificial intelligence
Abstract: Distributed estimation algorithms, when based on the mean-square error criterion, often encounter steady-state misalignment in scenarios where the adaptive network experiences impulsive noise. Addressing this challenge, this paper introduces the diffusion combinatoric correntropy algorithm, which employs the combinatoric correntropy as its cost function. Leveraging the inherent robustness of the combinatoric correntropy cost function, this algorithm effectively mitigates the negative impacts caused by impulse noise. A comprehensive analysis, including experimental simulations, is conducted to evaluate the performance of the diffusion combinatoric maximum correntropy criterion algorithm. The simulation outcomes demonstrate that the proposed algorithm surpasses the diffusion maximum correntropy criterion algorithm in terms of convergence speed and steady-state performance. These simulation results align closely with the theoretical analysis, further validating the effectiveness of the diffusion combinatoric correntropy algorithm. [ABSTRACT FROM AUTHOR]
Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Distributed estimation algorithms, when based on the mean-square error criterion, often encounter steady-state misalignment in scenarios where the adaptive network experiences impulsive noise. Addressing this challenge, this paper introduces the diffusion combinatoric correntropy algorithm, which employs the combinatoric correntropy as its cost function. Leveraging the inherent robustness of the combinatoric correntropy cost function, this algorithm effectively mitigates the negative impacts caused by impulse noise. A comprehensive analysis, including experimental simulations, is conducted to evaluate the performance of the diffusion combinatoric maximum correntropy criterion algorithm. The simulation outcomes demonstrate that the proposed algorithm surpasses the diffusion maximum correntropy criterion algorithm in terms of convergence speed and steady-state performance. These simulation results align closely with the theoretical analysis, further validating the effectiveness of the diffusion combinatoric correntropy algorithm. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Circuits, Systems & Signal Processing is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1007/s00034-024-02826-8
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      – Code: eng
        Text: English
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        Type: general
      – SubjectFull: Burst noise
        Type: general
      – SubjectFull: Distributed algorithms
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      – SubjectFull: System identification
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      – SubjectFull: Artificial intelligence
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            – D: 01
              M: 02
              Text: Feb2025
              Type: published
              Y: 2025
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            – TitleFull: Circuits, Systems & Signal Processing
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