Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications
A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost fun...
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| Veröffentlicht in: | IEEE transactions on circuits and systems. II, Express briefs Express briefs, 2019-11, Vol.66 (11), p.1915-1919 |
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| container_end_page | 1919 |
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| container_issue | 11 |
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| container_title | IEEE transactions on circuits and systems. II, Express briefs |
| container_volume | 66 |
| creator | Li, Yingsong Jiang, Zhengxiong Shi, Wanlu Han, Xiao Chen, Badong |
| description | A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. Simulation results indicate that the HNC-PNMCC is better than the PNMCC, MCC, and sparse MCC with respect to the estimation performance for the cluster-sparse system identification under the impulsive noises. |
| doi_str_mv | 10.1109/TCSII.2019.2891654 |
| format | Article |
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The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. 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II, Express briefs</title><addtitle>TCSII</addtitle><description>A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. 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II, Express briefs</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Li, Yingsong</au><au>Jiang, Zhengxiong</au><au>Shi, Wanlu</au><au>Han, Xiao</au><au>Chen, Badong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications</atitle><jtitle>IEEE transactions on circuits and systems. II, Express briefs</jtitle><stitle>TCSII</stitle><date>2019-11-01</date><risdate>2019</risdate><volume>66</volume><issue>11</issue><spage>1915</spage><epage>1919</epage><pages>1915-1919</pages><issn>1549-7747</issn><eissn>1558-3791</eissn><coden>ICSPE5</coden><abstract>A blocked proportionate normalized maximum correntropy criterion (PNMCC) is presented to improve the estimation behavior of the traditional maximum correntropy criterion (MCC) algorithm for identifying the blocked sparse systems. The proposed blocked MCC is implemented by constructing a new cost function based on a hybrid-norm constraint (HNC) of the filter coefficient vector to adaptively utilize the cluster-sparse characteristic of unknown systems, denoting as hybrid-norm constrained PNMCC (HNC-PNMCC). The proposed HNC-PNMCC algorithm is achieved by using the basis pursuit. Various simulations are brought out to confirm the validity of the HNC-PNMCC. Simulation results indicate that the HNC-PNMCC is better than the PNMCC, MCC, and sparse MCC with respect to the estimation performance for the cluster-sparse system identification under the impulsive noises.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TCSII.2019.2891654</doi><tpages>5</tpages><orcidid>https://orcid.org/0000-0003-3915-8043</orcidid><orcidid>https://orcid.org/0000-0003-1710-3818</orcidid><orcidid>https://orcid.org/0000-0003-3457-2077</orcidid><orcidid>https://orcid.org/0000-0002-2450-6028</orcidid></addata></record> |
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| subjects | <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">l Algorithms Circuits and systems cluster-sparse system Clustering algorithms Clusters Computer simulation Constraints Convergence Cost function Criteria Dispersion Estimation Hybrid systems impulsive noise environments Indexes Kernel Maximum correntropy criterion norm PNMCC algorithm System identification |
| title | Blocked Maximum Correntropy Criterion Algorithm for Cluster-Sparse System Identifications |
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