Kernel Adaptive Filters With Feedback Based on Maximum Correntropy
This paper presents novel kernel adaptive filters with feedback, namely, kernel recursive maximum correntropy with multiple feedback (KRMC-MF) and its simplified version, a linear recurrent kernel online learning algorithm based on maximum correntropy criterion (LRKOL-MCC). In LRKOL-MCC and KRMC-MF,...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2018-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8295208/ |
| _version_ | 1818742821076598784 |
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| author | Shiyuan Wang Lujuan Dang Wanli Wang Guobing Qian Chi K. Tse |
| author_facet | Shiyuan Wang Lujuan Dang Wanli Wang Guobing Qian Chi K. Tse |
| author_sort | Shiyuan Wang |
| collection | DOAJ |
| description | This paper presents novel kernel adaptive filters with feedback, namely, kernel recursive maximum correntropy with multiple feedback (KRMC-MF) and its simplified version, a linear recurrent kernel online learning algorithm based on maximum correntropy criterion (LRKOL-MCC). In LRKOL-MCC and KRMC-MF, single output and multiple outputs based on single delay are utilized to construct their feedback structure, respectively. Compared with the minimum mean square error criterion, the maximum correntropy criterion (MCC) adopted by LRKOL-MCC and KRMC-MF captures higher order statistics of errors. The proposed filters are, therefore, robust against outliers. Therefore, the past information can be reused to improve filtering performance in terms of the steady-state mean square error. The convergence characteristics of the filter parameters in LRKOL-MCC and KRMC-MF are also derived. Simulations on chaotic time-series prediction and nonlinear regression illustrate the desirable accuracy and robustness of the proposed filters. |
| first_indexed | 2024-12-18T02:18:36Z |
| format | Article |
| id | doaj.art-5751cd9eb4e14107834ee836091e3196 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T02:18:36Z |
| publishDate | 2018-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5751cd9eb4e14107834ee836091e31962022-12-21T21:24:20ZengIEEEIEEE Access2169-35362018-01-016105401055210.1109/ACCESS.2018.28082188295208Kernel Adaptive Filters With Feedback Based on Maximum CorrentropyShiyuan Wang0https://orcid.org/0000-0002-5028-5839Lujuan Dang1Wanli Wang2Guobing Qian3Chi K. Tse4College of Electronic and Information Engineering, Southwest University, Chongqing, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing, ChinaCollege of Electronic and Information Engineering, Southwest University, Chongqing, ChinaDepartment of Electronic and Information Engineering, The Hong Kong Polytechnic University, Hong KongThis paper presents novel kernel adaptive filters with feedback, namely, kernel recursive maximum correntropy with multiple feedback (KRMC-MF) and its simplified version, a linear recurrent kernel online learning algorithm based on maximum correntropy criterion (LRKOL-MCC). In LRKOL-MCC and KRMC-MF, single output and multiple outputs based on single delay are utilized to construct their feedback structure, respectively. Compared with the minimum mean square error criterion, the maximum correntropy criterion (MCC) adopted by LRKOL-MCC and KRMC-MF captures higher order statistics of errors. The proposed filters are, therefore, robust against outliers. Therefore, the past information can be reused to improve filtering performance in terms of the steady-state mean square error. The convergence characteristics of the filter parameters in LRKOL-MCC and KRMC-MF are also derived. Simulations on chaotic time-series prediction and nonlinear regression illustrate the desirable accuracy and robustness of the proposed filters.https://ieeexplore.ieee.org/document/8295208/Kernel adaptive filtersmaximum correntropyminimum mean square errorfeedback structureconvergence |
| spellingShingle | Shiyuan Wang Lujuan Dang Wanli Wang Guobing Qian Chi K. Tse Kernel Adaptive Filters With Feedback Based on Maximum Correntropy IEEE Access Kernel adaptive filters maximum correntropy minimum mean square error feedback structure convergence |
| title | Kernel Adaptive Filters With Feedback Based on Maximum Correntropy |
| title_full | Kernel Adaptive Filters With Feedback Based on Maximum Correntropy |
| title_fullStr | Kernel Adaptive Filters With Feedback Based on Maximum Correntropy |
| title_full_unstemmed | Kernel Adaptive Filters With Feedback Based on Maximum Correntropy |
| title_short | Kernel Adaptive Filters With Feedback Based on Maximum Correntropy |
| title_sort | kernel adaptive filters with feedback based on maximum correntropy |
| topic | Kernel adaptive filters maximum correntropy minimum mean square error feedback structure convergence |
| url | https://ieeexplore.ieee.org/document/8295208/ |
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