Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion
The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional H...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-10-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/17/10/7149 |
| _version_ | 1798042107043119104 |
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| author | Zongze Wu Siyuan Peng Badong Chen Haiquan Zhao |
| author_facet | Zongze Wu Siyuan Peng Badong Chen Haiquan Zhao |
| author_sort | Zongze Wu |
| collection | DOAJ |
| description | The maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm. |
| first_indexed | 2024-04-11T22:30:54Z |
| format | Article |
| id | doaj.art-265351ad5189469c84720f493e539922 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-11T22:30:54Z |
| publishDate | 2015-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-265351ad5189469c84720f493e5399222022-12-22T03:59:24ZengMDPI AGEntropy1099-43002015-10-0117107149716610.3390/e17107149e17107149Robust Hammerstein Adaptive Filtering under Maximum Correntropy CriterionZongze Wu0Siyuan Peng1Badong Chen2Haiquan Zhao3School of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Electronic and Information Engineering, South China University of Technology, Guangzhou 510640, ChinaSchool of Electronic and Information Engineering, Xi’an Jiaotong University, Xi’an 710049, ChinaSchool of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaThe maximum correntropy criterion (MCC) has recently been successfully applied to adaptive filtering. Adaptive algorithms under MCC show strong robustness against large outliers. In this work, we apply the MCC criterion to develop a robust Hammerstein adaptive filter. Compared with the traditional Hammerstein adaptive filters, which are usually derived based on the well-known mean square error (MSE) criterion, the proposed algorithm can achieve better convergence performance especially in the presence of impulsive non-Gaussian (e.g., α-stable) noises. Additionally, some theoretical results concerning the convergence behavior are also obtained. Simulation examples are presented to confirm the superior performance of the new algorithm.http://www.mdpi.com/1099-4300/17/10/7149Hammerstein adaptive filteringMCCnonlinear system identification |
| spellingShingle | Zongze Wu Siyuan Peng Badong Chen Haiquan Zhao Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion Entropy Hammerstein adaptive filtering MCC nonlinear system identification |
| title | Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion |
| title_full | Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion |
| title_fullStr | Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion |
| title_full_unstemmed | Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion |
| title_short | Robust Hammerstein Adaptive Filtering under Maximum Correntropy Criterion |
| title_sort | robust hammerstein adaptive filtering under maximum correntropy criterion |
| topic | Hammerstein adaptive filtering MCC nonlinear system identification |
| url | http://www.mdpi.com/1099-4300/17/10/7149 |
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