Component content soft-sensor based on SVM in rare earth countercurrent extraction process
The problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine in soft-sensor modeling. In consideration of the online measurement of the component content in rare earth counter-current extraction separation process, two algorithms o...
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| creator | Rongxiu Lu Hui Yang |
| description | The problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine in soft-sensor modeling. In consideration of the online measurement of the component content in rare earth counter-current extraction separation process, two algorithms of SVM and LS_SVM with RBF kennel was applied to the modeling of the rare-earth extraction separation process. Through comparing the simulations of two models, it shows that the component content soft-sensor model based on LS_SVM has both preferable generalization and high velocity. LS_SVM is an effective method for rare-earth extract process soft-sensor. |
| doi_str_mv | 10.1109/WCICA.2008.4594209 |
| format | Conference Proceeding |
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In consideration of the online measurement of the component content in rare earth counter-current extraction separation process, two algorithms of SVM and LS_SVM with RBF kennel was applied to the modeling of the rare-earth extraction separation process. Through comparing the simulations of two models, it shows that the component content soft-sensor model based on LS_SVM has both preferable generalization and high velocity. LS_SVM is an effective method for rare-earth extract process soft-sensor.</description><subject>Artificial neural networks</subject><subject>Automation</subject><subject>counter current extraction</subject><subject>Earth</subject><subject>Kernel</subject><subject>LS_SVM</subject><subject>Machine learning</subject><subject>modeling</subject><subject>Separation processes</subject><subject>soft-sensor</subject><subject>Support vector machines</subject><isbn>1424421136</isbn><isbn>9781424421138</isbn><isbn>1424421144</isbn><isbn>9781424421145</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2008</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpFkN1KxDAQhSOyoLvuC-hNXqDrJJ0kzaUU_2DFC__AmyXNTrHiNiXJgr69LS54bg4HvhkOh7FzASshwF6-1ff11UoCVCtUFiXYIzYXKBGlEIjH_6HUMzafQAugDZywZUqfMApVqa0-Ze912A2hpz5zH_o8eQptLhL1KUTeuERbHnr-9PrAu55HF4mTi_ljxPcjH_0-xumKvnN0PncjO8TgKaUzNmvdV6LlwRfs5eb6ub4r1o-3Y_910QmjcqGMd1iOhcm0YKVSUCnpq0ajNhIbkKb1WDmQbVXCVsqGtKmMcn7rJRrblgt28fe3I6LNELudiz-bwzDlLzV6VUo</recordid><startdate>200806</startdate><enddate>200806</enddate><creator>Rongxiu Lu</creator><creator>Hui Yang</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>200806</creationdate><title>Component content soft-sensor based on SVM in rare earth countercurrent extraction process</title><author>Rongxiu Lu ; Hui Yang</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-57ca43142e7f092550852c8b646724b027fc48a02f830d22be67875acdc2479f3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>chi ; eng</language><creationdate>2008</creationdate><topic>Artificial neural networks</topic><topic>Automation</topic><topic>counter current extraction</topic><topic>Earth</topic><topic>Kernel</topic><topic>LS_SVM</topic><topic>Machine learning</topic><topic>modeling</topic><topic>Separation processes</topic><topic>soft-sensor</topic><topic>Support vector machines</topic><toplevel>online_resources</toplevel><creatorcontrib>Rongxiu Lu</creatorcontrib><creatorcontrib>Hui Yang</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>Rongxiu Lu</au><au>Hui Yang</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Component content soft-sensor based on SVM in rare earth countercurrent extraction process</atitle><btitle>2008 7th World Congress on Intelligent Control and Automation</btitle><stitle>WCICA</stitle><date>2008-06</date><risdate>2008</risdate><spage>8184</spage><epage>8187</epage><pages>8184-8187</pages><isbn>1424421136</isbn><isbn>9781424421138</isbn><eisbn>1424421144</eisbn><eisbn>9781424421145</eisbn><abstract>The problems of small sample, non-linearity, high dimensions and local minimal value can be well solved by support vector machine in soft-sensor modeling. In consideration of the online measurement of the component content in rare earth counter-current extraction separation process, two algorithms of SVM and LS_SVM with RBF kennel was applied to the modeling of the rare-earth extraction separation process. Through comparing the simulations of two models, it shows that the component content soft-sensor model based on LS_SVM has both preferable generalization and high velocity. LS_SVM is an effective method for rare-earth extract process soft-sensor.</abstract><pub>IEEE</pub><doi>10.1109/WCICA.2008.4594209</doi><tpages>4</tpages></addata></record> |
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| language | chi ; eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Automation counter current extraction Earth Kernel LS_SVM Machine learning modeling Separation processes soft-sensor Support vector machines |
| title | Component content soft-sensor based on SVM in rare earth countercurrent extraction process |
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