Performance and Analysis of Recursive Constrained Least Lncosh Algorithm Under Impulsive Noises
We propose a recursive constrained least lncosh (RCLL) adaptive algorithm to combat the impulsive noises. In general, the lncosh function is used to develop a new algorithm within the context of constrained adaptive filtering via solving a linear constrained optimization problem, where the lncosh fu...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2021-06, Vol.68 (6), p.2217-2221 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
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| creator | Liang, Tao Li, Yingsong Xue, Wei Li, Yibing Jiang, Tao |
| description | We propose a recursive constrained least lncosh (RCLL) adaptive algorithm to combat the impulsive noises. In general, the lncosh function is used to develop a new algorithm within the context of constrained adaptive filtering via solving a linear constrained optimization problem, where the lncosh function is a natural logarithm of hyperbolic cosine function, which can be regarded as a combination of mean-square-error (MSE) and mean-absolute-error (MAE) criteria. Compared with other typical recursive methods, the proposed RCLL algorithm can obtain superior steady state behavior and better robustness for combating impulsive noises. Besides, the mean-square convergence condition and theoretical transient mean-square-deviation of the RCLL algorithm is presented. Simulation results verified the theoretical analysis in non-Gaussian noises and shown the superior performance of the proposed RCLL algorithm. |
| doi_str_mv | 10.1109/TCSII.2020.3037877 |
| format | Article |
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Simulation results verified the theoretical analysis in non-Gaussian noises and shown the superior performance of the proposed RCLL algorithm.</description><identifier>ISSN: 1549-7747</identifier><identifier>EISSN: 1558-3791</identifier><identifier>DOI: 10.1109/TCSII.2020.3037877</identifier><identifier>CODEN: ICSPE5</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Adaptive algorithms ; Adaptive filters ; Algorithms ; Circuits and systems ; Convergence ; Gaussian noise ; Hyperbolic functions ; linear system identification ; Lncosh cost function ; Optimization ; Optimized production technology ; recursive constrained adaptive filtering ; Recursive methods ; Robustness ; Signal processing algorithms ; Steady-state ; Trigonometric functions</subject><ispartof>IEEE transactions on circuits and systems. II, Express briefs, 2021-06, Vol.68 (6), p.2217-2221</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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II, Express briefs</title><addtitle>TCSII</addtitle><description>We propose a recursive constrained least lncosh (RCLL) adaptive algorithm to combat the impulsive noises. In general, the lncosh function is used to develop a new algorithm within the context of constrained adaptive filtering via solving a linear constrained optimization problem, where the lncosh function is a natural logarithm of hyperbolic cosine function, which can be regarded as a combination of mean-square-error (MSE) and mean-absolute-error (MAE) criteria. Compared with other typical recursive methods, the proposed RCLL algorithm can obtain superior steady state behavior and better robustness for combating impulsive noises. Besides, the mean-square convergence condition and theoretical transient mean-square-deviation of the RCLL algorithm is presented. Simulation results verified the theoretical analysis in non-Gaussian noises and shown the superior performance of the proposed RCLL algorithm.</description><subject>Adaptive algorithms</subject><subject>Adaptive filters</subject><subject>Algorithms</subject><subject>Circuits and systems</subject><subject>Convergence</subject><subject>Gaussian noise</subject><subject>Hyperbolic functions</subject><subject>linear system identification</subject><subject>Lncosh cost function</subject><subject>Optimization</subject><subject>Optimized production technology</subject><subject>recursive constrained adaptive filtering</subject><subject>Recursive methods</subject><subject>Robustness</subject><subject>Signal processing algorithms</subject><subject>Steady-state</subject><subject>Trigonometric functions</subject><issn>1549-7747</issn><issn>1558-3791</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNo9kEtLw0AUhYMoWKt_QDcDrlPn2ZlZluAjEFS0XQ8xc8emJJk6kwj996YPXN27ON-B8yXJLcEzQrB-WGafeT6jmOIZw0wqKc-SCRFCpUxqcr7_uU6l5PIyuYpxgzHVmNFJYt4hOB_asqsAlZ1Fi65sdrGOyDv0AdUQYv0LKPNd7ENZd2BRAWXsUdFVPq7Rovn2oe7XLVp1FgLK2-3QHJBXX0eI18mFK5sIN6c7TVZPj8vsJS3envNsUaQV1aJPFQhFnVMKuLaCgsLCWasIt5Z9USkZSO6ktKSaK064UJXgXFV6ThR2kmo2Te6PvdvgfwaIvdn4IYxboqGCMa7HwWpM0WOqCj7GAM5sQ92WYWcINnuR5iDS7EWak8gRujtCNQD8A5oKjZVgf21ebyE</recordid><startdate>20210601</startdate><enddate>20210601</enddate><creator>Liang, Tao</creator><creator>Li, Yingsong</creator><creator>Xue, Wei</creator><creator>Li, Yibing</creator><creator>Jiang, Tao</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Liang, Tao</au><au>Li, Yingsong</au><au>Xue, Wei</au><au>Li, Yibing</au><au>Jiang, Tao</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Performance and Analysis of Recursive Constrained Least Lncosh Algorithm Under Impulsive Noises</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><date>2021-06-01</date><risdate>2021</risdate><volume>68</volume><issue>6</issue><spage>2217</spage><epage>2221</epage><pages>2217-2221</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ICSPE5</coden><abstract>We propose a recursive constrained least lncosh (RCLL) adaptive algorithm to combat the impulsive noises. In general, the lncosh function is used to develop a new algorithm within the context of constrained adaptive filtering via solving a linear constrained optimization problem, where the lncosh function is a natural logarithm of hyperbolic cosine function, which can be regarded as a combination of mean-square-error (MSE) and mean-absolute-error (MAE) criteria. Compared with other typical recursive methods, the proposed RCLL algorithm can obtain superior steady state behavior and better robustness for combating impulsive noises. Besides, the mean-square convergence condition and theoretical transient mean-square-deviation of the RCLL algorithm is presented. 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| subjects | Adaptive algorithms Adaptive filters Algorithms Circuits and systems Convergence Gaussian noise Hyperbolic functions linear system identification Lncosh cost function Optimization Optimized production technology recursive constrained adaptive filtering Recursive methods Robustness Signal processing algorithms Steady-state Trigonometric functions |
| title | Performance and Analysis of Recursive Constrained Least Lncosh Algorithm Under Impulsive Noises |
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