A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification
A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero...
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| Format: | Article |
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MDPI AG
2017-01-01
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| Series: | Entropy |
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| Online Access: | http://www.mdpi.com/1099-4300/19/1/45 |
| _version_ | 1817996428833718272 |
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| author | Yingsong Li Yanyan Wang Rui Yang Felix Albu |
| author_facet | Yingsong Li Yanyan Wang Rui Yang Felix Albu |
| author_sort | Yingsong Li |
| collection | DOAJ |
| description | A soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance. |
| first_indexed | 2024-04-14T02:22:08Z |
| format | Article |
| id | doaj.art-8e9ace27fe054274b7f07e60320f0882 |
| institution | Directory Open Access Journal |
| issn | 1099-4300 |
| language | English |
| last_indexed | 2024-04-14T02:22:08Z |
| publishDate | 2017-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Entropy |
| spelling | doaj.art-8e9ace27fe054274b7f07e60320f08822022-12-22T02:17:59ZengMDPI AGEntropy1099-43002017-01-011914510.3390/e19010045e19010045A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System IdentificationYingsong Li0Yanyan Wang1Rui Yang2Felix Albu3College of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Information and Communications Engineering, Harbin Engineering University, Harbin 150001, ChinaCollege of Engineering, Huazhong Agricultural University, Wuhan 430070, ChinaDepartment of Electronics, Valahia University of Targoviste, Targoviste 130082, RomaniaA soft parameter function penalized normalized maximum correntropy criterion (SPF-NMCC) algorithm is proposed for sparse system identification. The proposed SPF-NMCC algorithm is derived on the basis of the normalized adaptive filter theory, the maximum correntropy criterion (MCC) algorithm and zero-attracting techniques. A soft parameter function is incorporated into the cost function of the traditional normalized MCC (NMCC) algorithm to exploit the sparsity properties of the sparse signals. The proposed SPF-NMCC algorithm is mathematically derived in detail. As a result, the proposed SPF-NMCC algorithm can provide an efficient zero attractor term to effectively attract the zero taps and near-zero coefficients to zero, and, hence, it can speed up the convergence. Furthermore, the estimation behaviors are obtained by estimating a sparse system and a sparse acoustic echo channel. Computer simulation results indicate that the proposed SPF-NMCC algorithm can achieve a better performance in comparison with the MCC, NMCC, LMS (least mean square) algorithms and their zero attraction forms in terms of both convergence speed and steady-state performance.http://www.mdpi.com/1099-4300/19/1/45adaptive filtersmaximum correntropy criterionkernel frameworksparse adaptive filteringsoft parameter functionzero attracting algorithm |
| spellingShingle | Yingsong Li Yanyan Wang Rui Yang Felix Albu A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification Entropy adaptive filters maximum correntropy criterion kernel framework sparse adaptive filtering soft parameter function zero attracting algorithm |
| title | A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification |
| title_full | A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification |
| title_fullStr | A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification |
| title_full_unstemmed | A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification |
| title_short | A Soft Parameter Function Penalized Normalized Maximum Correntropy Criterion Algorithm for Sparse System Identification |
| title_sort | soft parameter function penalized normalized maximum correntropy criterion algorithm for sparse system identification |
| topic | adaptive filters maximum correntropy criterion kernel framework sparse adaptive filtering soft parameter function zero attracting algorithm |
| url | http://www.mdpi.com/1099-4300/19/1/45 |
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