Robust Sequential Learning Algorithm for Function Approximation Base on Strong Tracking Filter
This paper addresses the problem that network whose parameters are updated using EKF can not obtain robust performance if the system state saltates when EKF reach stable state. Strong tracking filter which introduces suboptimal fading factor matrix to overcome the problem is utilized to adjust the n...
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| creator | Huaiqi Kang Caicheng Shi Peikun He Baojun Zhao |
| description | This paper addresses the problem that network whose parameters are updated using EKF can not obtain robust performance if the system state saltates when EKF reach stable state. Strong tracking filter which introduces suboptimal fading factor matrix to overcome the problem is utilized to adjust the network parameters to obtain robust performance. The winner neuron updating strategy is also employed to reduce the computation load for online application. Experimental results show the proposed algorithm can achieve smaller approximation error and more compact network structure than several other typical sequential learning algorithms |
| doi_str_mv | 10.1109/ICOSP.2006.345924 |
| format | Conference Proceeding |
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Strong tracking filter which introduces suboptimal fading factor matrix to overcome the problem is utilized to adjust the network parameters to obtain robust performance. The winner neuron updating strategy is also employed to reduce the computation load for online application. Experimental results show the proposed algorithm can achieve smaller approximation error and more compact network structure than several other typical sequential learning algorithms</description><identifier>ISSN: 2164-5221</identifier><identifier>ISBN: 0780397363</identifier><identifier>ISBN: 9780780397361</identifier><identifier>EISBN: 9780780397378</identifier><identifier>EISBN: 0780397371</identifier><identifier>DOI: 10.1109/ICOSP.2006.345924</identifier><identifier>LCCN: 06-927929</identifier><language>eng</language><subject>Approximation algorithms ; Approximation error ; Covariance matrix ; Fading ; Filters ; Function approximation ; Neurons ; Nonlinear systems ; Radio access networks ; Robustness</subject><ispartof>2006 8th international Conference on Signal Processing, 2006, Vol.3</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4129219$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,2058,4050,4051,27925,54920</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4129219$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Huaiqi Kang</creatorcontrib><creatorcontrib>Caicheng Shi</creatorcontrib><creatorcontrib>Peikun He</creatorcontrib><creatorcontrib>Baojun Zhao</creatorcontrib><title>Robust Sequential Learning Algorithm for Function Approximation Base on Strong Tracking Filter</title><title>2006 8th international Conference on Signal Processing</title><addtitle>ICOSP</addtitle><description>This paper addresses the problem that network whose parameters are updated using EKF can not obtain robust performance if the system state saltates when EKF reach stable state. Strong tracking filter which introduces suboptimal fading factor matrix to overcome the problem is utilized to adjust the network parameters to obtain robust performance. The winner neuron updating strategy is also employed to reduce the computation load for online application. Experimental results show the proposed algorithm can achieve smaller approximation error and more compact network structure than several other typical sequential learning algorithms</description><subject>Approximation algorithms</subject><subject>Approximation error</subject><subject>Covariance matrix</subject><subject>Fading</subject><subject>Filters</subject><subject>Function approximation</subject><subject>Neurons</subject><subject>Nonlinear systems</subject><subject>Radio access networks</subject><subject>Robustness</subject><issn>2164-5221</issn><isbn>0780397363</isbn><isbn>9780780397361</isbn><isbn>9780780397378</isbn><isbn>0780397371</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2006</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1j9tKw0AYhFe0YK19APFmXyDx30P-3b2sxWqhULG5tmzSTV1Nk7rZgL698QQDw1x8wwwhVwxSxsDcLOfrzWPKATAVMjNcnpCpURoGCaOE0qfk4j-gOCNjzlAmGedsRMaAieHKcHNOpl33CgCCaY0Cx-T5qS36LtKNe-9dE72t6crZ0PhmT2f1vg0-vhxo1Qa66Jsy-rahs-MxtB_-YH_Sre0cHXwTQzswebDl2ze88HV04ZKMKlt3bvrnE5Iv7vL5Q7Ja3y_ns1XimcpigpVhmTZ8V0gwRYYaERQ4XkGlh1OoXekKke2YsQUwVBYra3daylIzq1BMyPVvrXfObY9hGBc-t5Jxw5kRX1C2WN4</recordid><startdate>2006</startdate><enddate>2006</enddate><creator>Huaiqi Kang</creator><creator>Caicheng Shi</creator><creator>Peikun He</creator><creator>Baojun Zhao</creator><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2006</creationdate><title>Robust Sequential Learning Algorithm for Function Approximation Base on Strong Tracking Filter</title><author>Huaiqi Kang ; Caicheng Shi ; Peikun He ; Baojun Zhao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-6f915892db409b56866070e2f0f878068eceb35d19ab0167a6faad844c81a763</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2006</creationdate><topic>Approximation algorithms</topic><topic>Approximation error</topic><topic>Covariance matrix</topic><topic>Fading</topic><topic>Filters</topic><topic>Function approximation</topic><topic>Neurons</topic><topic>Nonlinear systems</topic><topic>Radio access networks</topic><topic>Robustness</topic><toplevel>online_resources</toplevel><creatorcontrib>Huaiqi Kang</creatorcontrib><creatorcontrib>Caicheng Shi</creatorcontrib><creatorcontrib>Peikun He</creatorcontrib><creatorcontrib>Baojun Zhao</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Huaiqi Kang</au><au>Caicheng Shi</au><au>Peikun He</au><au>Baojun Zhao</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Robust Sequential Learning Algorithm for Function Approximation Base on Strong Tracking Filter</atitle><btitle>2006 8th international Conference on Signal Processing</btitle><stitle>ICOSP</stitle><date>2006</date><risdate>2006</risdate><volume>3</volume><issn>2164-5221</issn><isbn>0780397363</isbn><isbn>9780780397361</isbn><eisbn>9780780397378</eisbn><eisbn>0780397371</eisbn><abstract>This paper addresses the problem that network whose parameters are updated using EKF can not obtain robust performance if the system state saltates when EKF reach stable state. Strong tracking filter which introduces suboptimal fading factor matrix to overcome the problem is utilized to adjust the network parameters to obtain robust performance. The winner neuron updating strategy is also employed to reduce the computation load for online application. Experimental results show the proposed algorithm can achieve smaller approximation error and more compact network structure than several other typical sequential learning algorithms</abstract><doi>10.1109/ICOSP.2006.345924</doi></addata></record> |
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| identifier | ISSN: 2164-5221 |
| ispartof | 2006 8th international Conference on Signal Processing, 2006, Vol.3 |
| issn | 2164-5221 |
| language | eng |
| recordid | cdi_ieee_primary_4129219 |
| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation algorithms Approximation error Covariance matrix Fading Filters Function approximation Neurons Nonlinear systems Radio access networks Robustness |
| title | Robust Sequential Learning Algorithm for Function Approximation Base on Strong Tracking Filter |
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