A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm
Impulsive noises are widely existing in various systems like noise cancellation system and wireless communication systems, where adaptive filtering (AF) is always employed to identify specific systems. Additionally, the impulsive noises will affect the performance for estimating these systems, resul...
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| Format: | Article |
| Language: | English |
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IEEE
2020-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8990071/ |
| _version_ | 1818330245544017920 |
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| author | Ying Guo Bing Ma Yingsong Li |
| author_facet | Ying Guo Bing Ma Yingsong Li |
| author_sort | Ying Guo |
| collection | DOAJ |
| description | Impulsive noises are widely existing in various systems like noise cancellation system and wireless communication systems, where adaptive filtering (AF) is always employed to identify specific systems. Additionally, the impulsive noises will affect the performance for estimating these systems, resulting in slow convergence or worse identification accuracy. In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCC<sub>adapt</sub> algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC<sub>adapt</sub> algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCC<sub>adapt</sub> algorithm suitable for sparse system identifications, the DMCC<sub>adapt</sub> algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC<sub>adapt</sub>). The theoretical analysis and simulation results are presented to show that the DPMCC<sub>adapt</sub> and DMCC<sub>adapt</sub> algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems. |
| first_indexed | 2024-12-13T13:00:53Z |
| format | Article |
| id | doaj.art-efdf9721e7374abeb510987d36cbbafa |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-13T13:00:53Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-efdf9721e7374abeb510987d36cbbafa2022-12-21T23:45:03ZengIEEEIEEE Access2169-35362020-01-018335743358710.1109/ACCESS.2020.29729058990071A Kernel-Width Adaption Diffusion Maximum Correntropy AlgorithmYing Guo0https://orcid.org/0000-0002-7934-3991Bing Ma1https://orcid.org/0000-0002-4264-0132Yingsong Li2https://orcid.org/0000-0002-2450-6028School of Information Science and Engineering, Shenyang University of Technology, Shenyang, ChinaSchool of Information Science and Engineering, Shenyang University of Technology, Shenyang, ChinaCollege of Information and Communication Engineering, Harbin Engineering University, Harbin, ChinaImpulsive noises are widely existing in various systems like noise cancellation system and wireless communication systems, where adaptive filtering (AF) is always employed to identify specific systems. Additionally, the impulsive noises will affect the performance for estimating these systems, resulting in slow convergence or worse identification accuracy. In this paper, a diffusion maximum correntropy criterion (DMCC) algorithm with adaption kernel width is proposed, denoting as DMCC<sub>adapt</sub> algorithm, to find out a solution for dynamically choosing the kernel width. The DMCC<sub>adapt</sub> algorithm chooses small kernel width at initial stage to improve its convergence speed rate, and uses large kernel width at completion stage to reduce its steady-state error. To render the proposed DMCC<sub>adapt</sub> algorithm suitable for sparse system identifications, the DMCC<sub>adapt</sub> algorithm based on proportional coefficient adjustment is realized and named as diffusion proportional maximum correntropy criterion (DPMCC<sub>adapt</sub>). The theoretical analysis and simulation results are presented to show that the DPMCC<sub>adapt</sub> and DMCC<sub>adapt</sub> algorithms have better convergence than the traditional diffusion AF algorithms under impulse noise and sparse systems.https://ieeexplore.ieee.org/document/8990071/Adaptive kernel widthdiffusion algorithmimpulse noisemaximum correntropy criterionsparse system identification |
| spellingShingle | Ying Guo Bing Ma Yingsong Li A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm IEEE Access Adaptive kernel width diffusion algorithm impulse noise maximum correntropy criterion sparse system identification |
| title | A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm |
| title_full | A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm |
| title_fullStr | A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm |
| title_full_unstemmed | A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm |
| title_short | A Kernel-Width Adaption Diffusion Maximum Correntropy Algorithm |
| title_sort | kernel width adaption diffusion maximum correntropy algorithm |
| topic | Adaptive kernel width diffusion algorithm impulse noise maximum correntropy criterion sparse system identification |
| url | https://ieeexplore.ieee.org/document/8990071/ |
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