The variable step‐size LMS/F algorithm using nonparametric method for adaptive system identification
Summary A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applic...
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| Published in | International journal of adaptive control and signal processing Vol. 34; no. 12; pp. 1799 - 1811 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
Hoboken, USA
John Wiley & Sons, Inc
01.12.2020
Wiley Subscription Services, Inc |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0890-6327 1099-1115 |
| DOI | 10.1002/acs.3185 |
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| Abstract | Summary
A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step‐size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step‐size equation, this article proposes the nonparametric variable step‐size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real‐time setup validate that the proposed NPVSS‐LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise. |
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| AbstractList | A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step‐size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step‐size equation, this article proposes the nonparametric variable step‐size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real‐time setup validate that the proposed NPVSS‐LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise. Summary A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are influenced by Gaussian noise and impulsive noise. Identification of these nonlinear physical systems in the presence of noise offers broader applications than linear system identification. Hence, this article introduces a variable step‐size technique to solve the conflicting requirement of rapid convergence and low mean square error (MSE) in the presence of both Gaussian and impulsive noise. Moreover, to avoid over parameterized equations existing in the variable step‐size equation, this article proposes the nonparametric variable step‐size (NPVSS), which depends on error estimates at instants of time and is used with the least mean square/fourth (LMS/F) algorithm. The computational complexity analysis, computer simulations, and implementation in real‐time setup validate that the proposed NPVSS‐LMS/F algorithm provides superior performance in terms of convergence time and MSE compared to the existing algorithms for both linear and nonlinear system identification in the presence of noise. |
| Author | Patnaik, Ansuman Nanda, Sarita |
| Author_xml | – sequence: 1 givenname: Ansuman orcidid: 0000-0002-2554-3736 surname: Patnaik fullname: Patnaik, Ansuman organization: KIIT University – sequence: 2 givenname: Sarita orcidid: 0000-0002-1671-5946 surname: Nanda fullname: Nanda, Sarita email: sarita22579@rediffmail.com organization: KIIT University |
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| CitedBy_id | crossref_primary_10_1016_j_phycom_2024_102482 crossref_primary_10_1007_s42417_023_00885_w crossref_primary_10_3390_machines10080670 crossref_primary_10_1007_s11760_024_03024_4 crossref_primary_10_1002_acs_3375 crossref_primary_10_1007_s00202_021_01438_8 crossref_primary_10_1007_s11760_023_02729_2 crossref_primary_10_1121_10_0006787 crossref_primary_10_1016_j_apacoust_2023_109668 crossref_primary_10_1109_JESTPE_2022_3146210 |
| Cites_doi | 10.1007/s00034-016-0356-x 10.1109/5.75086 10.1109/TNNLS.2011.2178446 10.1016/j.sigpro.2013.06.018 10.1049/el:19970311 10.1109/LSP.2003.821722 10.1109/ICASSP.2006.1660624 10.1109/TCSI.2016.2572091 10.1016/j.sigpro.2014.05.018 10.1109/78.934136 10.1109/ICDSP.2015.7251952 10.1002/9780470740156 10.1109/79.127998 10.1109/TCSII.2016.2531159 10.1109/PROC.1976.10286 10.1109/5.231338 10.1109/TIT.1984.1056886 10.1016/j.phycom.2012.04.002 10.1016/j.sigpro.2014.10.037 10.1109/97.554469 10.1109/LSP.2006.876323 10.1109/TASLP.2014.2324175 10.1109/LSP.2014.2325495 10.1002/dac.2517 10.1016/j.amc.2011.02.026 |
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A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are... A fundamental challenge affecting the performance of a system is the undesired effect of noise on the system. Practically, real‐time systems are influenced by... |
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| SubjectTerms | Adaptive algorithms Adaptive systems Algorithms Convergence Gaussian noise impulsive noise Noise Nonlinear systems nonparametric Nonparametric statistics Random noise System identification variable step‐size Volterra modeling |
| Title | The variable step‐size LMS/F algorithm using nonparametric method for adaptive system identification |
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