Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm
To fully take advantage of LMS, LMAT, and SELMS, a novel adaptive estimator using the L1-norm and L0-norm of the estimated error is proposed in this paper. Then based on minimizing the mean-square deviation at the current time, the optimal step-size, parameters δ and θ of the proposed adaptive estim...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2023-05-01
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| Series: | Journal of Automation and Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2949855423000187 |
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| author | Sihai Guan Chuanwu Zhang Guofu Wang Bharat Biswal |
| author_facet | Sihai Guan Chuanwu Zhang Guofu Wang Bharat Biswal |
| author_sort | Sihai Guan |
| collection | DOAJ |
| description | To fully take advantage of LMS, LMAT, and SELMS, a novel adaptive estimator using the L1-norm and L0-norm of the estimated error is proposed in this paper. Then based on minimizing the mean-square deviation at the current time, the optimal step-size, parameters δ and θ of the proposed adaptive estimator are obtained. Besides, the stability and computational complexity of the mean estimation error is analyzed theoretically. Experimental results (both simulation and real mechanical system datasets) show that the proposed adaptive estimator is more robust to input signals and a variety of measurement noises (Gaussian and non-Gaussian noises). In addition, it is superior to LMS, LMAT, SELMS, the convex combination of LMS and LMAT algorithm, the convex combination of LMS and SELMS algorithm, and the convex combination of SELMS and LMAT algorithm. The theoretical analysis is consistent with the Monte-Carlo results. Both of them show that the adaptive estimator has an excellent performance in the estimation of unknown linear systems under various measurement noises. |
| format | Article |
| id | doaj-art-b863abff761b4e11beef98c1a08e9ee4 |
| institution | Kabale University |
| issn | 2949-8554 |
| language | English |
| publishDate | 2023-05-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Automation and Intelligence |
| spelling | doaj-art-b863abff761b4e11beef98c1a08e9ee42025-08-20T03:42:43ZengKeAi Communications Co., Ltd.Journal of Automation and Intelligence2949-85542023-05-012210511710.1016/j.jai.2023.06.004Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-normSihai Guan0Chuanwu Zhang1Guofu Wang2Bharat Biswal3Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China; College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China; Corresponding author at: College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, China.Key Laboratory of Electronic and Information Engineering, State Ethnic Affairs Commission, Chengdu, 610041, China; College of Electronic and Information, Southwest Minzu University, Chengdu, 610041, ChinaCenter for Materials Science and Engineering, School of Electrical and Information Engineering, Guangxi University of Science and Technology, Liuzhou, 545006, ChinaDepartment of Biomedical Engineering, New Jersey Institute of Technology, Newark, NJ, 07102, USA; The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; Corresponding author at: The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Laboratory for Neuroinformation, Center for Information in Medicine, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China.To fully take advantage of LMS, LMAT, and SELMS, a novel adaptive estimator using the L1-norm and L0-norm of the estimated error is proposed in this paper. Then based on minimizing the mean-square deviation at the current time, the optimal step-size, parameters δ and θ of the proposed adaptive estimator are obtained. Besides, the stability and computational complexity of the mean estimation error is analyzed theoretically. Experimental results (both simulation and real mechanical system datasets) show that the proposed adaptive estimator is more robust to input signals and a variety of measurement noises (Gaussian and non-Gaussian noises). In addition, it is superior to LMS, LMAT, SELMS, the convex combination of LMS and LMAT algorithm, the convex combination of LMS and SELMS algorithm, and the convex combination of SELMS and LMAT algorithm. The theoretical analysis is consistent with the Monte-Carlo results. Both of them show that the adaptive estimator has an excellent performance in the estimation of unknown linear systems under various measurement noises.http://www.sciencedirect.com/science/article/pii/S2949855423000187Adaptive filterLMSLMATSELMSMultiple types of noises |
| spellingShingle | Sihai Guan Chuanwu Zhang Guofu Wang Bharat Biswal Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm Journal of Automation and Intelligence Adaptive filter LMS LMAT SELMS Multiple types of noises |
| title | Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm |
| title_full | Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm |
| title_fullStr | Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm |
| title_full_unstemmed | Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm |
| title_short | Robust adaptive estimator based on a novel objective function—Using the L1-norm and L0-norm |
| title_sort | robust adaptive estimator based on a novel objective function using the l1 norm and l0 norm |
| topic | Adaptive filter LMS LMAT SELMS Multiple types of noises |
| url | http://www.sciencedirect.com/science/article/pii/S2949855423000187 |
| work_keys_str_mv | AT sihaiguan robustadaptiveestimatorbasedonanovelobjectivefunctionusingthel1normandl0norm AT chuanwuzhang robustadaptiveestimatorbasedonanovelobjectivefunctionusingthel1normandl0norm AT guofuwang robustadaptiveestimatorbasedonanovelobjectivefunctionusingthel1normandl0norm AT bharatbiswal robustadaptiveestimatorbasedonanovelobjectivefunctionusingthel1normandl0norm |