A identification method of a nonlinear ARX model with variable order for nonlinear systems
This paper gives a identification method of new input-output model. In a identification of a nonlinear model, a nonlinear ARX model(NARX) is presented by Ohata, Furuta et.al. The NARX model consists of a set of ARX models with same orders at each output level. However, a systems order of a nonlinear...
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| creator | Hasuike, Yuya Izutsu, Masaki Hatakeyama, Shosiro |
| description | This paper gives a identification method of new input-output model. In a identification of a nonlinear model, a nonlinear ARX model(NARX) is presented by Ohata, Furuta et.al. The NARX model consists of a set of ARX models with same orders at each output level. However, a systems order of a nonlinear system is different for each system state, usually. We propose new NARX model with variable order at a output levels. In addition, the proposed method is compared with the conventional NARX model by estimated accuracy. As a result, the conformance rate of the proposed method were larger than that by one of the NARX model. Furthermore, the mean and the variance of estimated error of the proposed method were smaller than one of the NARX model. |
| doi_str_mv | 10.1109/IECON.2013.6699648 |
| format | Conference Proceeding |
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Furthermore, the mean and the variance of estimated error of the proposed method were smaller than one of the NARX model.</description><subject>Accuracy</subject><subject>Data models</subject><subject>Equations</subject><subject>Interpolation</subject><subject>Mathematical model</subject><subject>Nonlinear systems</subject><subject>Predictive models</subject><issn>1553-572X</issn><isbn>1479902241</isbn><isbn>9781479902248</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2013</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNpNkM1KAzEURiMo2FZfQDd5gRlz8zeT5VCqLRQLolDclMzkDo3MTCQJSt9ewS5cfZtzzuIj5A5YCcDMw2a13D2XnIEotTZGy_qCzEFWxjDOJVySGSglClXx_TWZp_TBmJK1hhl5b6h3OGXf-85mHyY6Yj4GR0NPLZ3CNPgJbaTNy56OweFAv30-0i8bvW0HpCE6jLQP8R-bTinjmG7IVW-HhLfnXZC3x9Xrcl1sd0-bZbMtPFQqF04gtEZbQCGZ5J1tRa00E6gUFw567LSxwCqrAIR2DGsmXc9aDZ2qf1WxIPd_XY-Ih8_oRxtPh_MP4gesD1KF</recordid><startdate>201311</startdate><enddate>201311</enddate><creator>Hasuike, Yuya</creator><creator>Izutsu, Masaki</creator><creator>Hatakeyama, Shosiro</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope></search><sort><creationdate>201311</creationdate><title>A identification method of a nonlinear ARX model with variable order for nonlinear systems</title><author>Hasuike, Yuya ; Izutsu, Masaki ; Hatakeyama, Shosiro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-d3e1b96a1e34042cab385603e5523d1fec69a107a51136d0e804df0b61c58e1b3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Accuracy</topic><topic>Data models</topic><topic>Equations</topic><topic>Interpolation</topic><topic>Mathematical model</topic><topic>Nonlinear systems</topic><topic>Predictive models</topic><toplevel>online_resources</toplevel><creatorcontrib>Hasuike, Yuya</creatorcontrib><creatorcontrib>Izutsu, Masaki</creatorcontrib><creatorcontrib>Hatakeyama, Shosiro</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan (POP) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Electronic Library (IEL)</collection><collection>IEEE Proceedings Order Plans (POP) 1998-present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hasuike, Yuya</au><au>Izutsu, Masaki</au><au>Hatakeyama, Shosiro</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>A identification method of a nonlinear ARX model with variable order for nonlinear systems</atitle><btitle>IECON 2013 - 39th Annual Conference of the IEEE Industrial Electronics Society</btitle><stitle>IECON</stitle><date>2013-11</date><risdate>2013</risdate><spage>3246</spage><epage>3251</epage><pages>3246-3251</pages><issn>1553-572X</issn><eisbn>1479902241</eisbn><eisbn>9781479902248</eisbn><abstract>This paper gives a identification method of new input-output model. In a identification of a nonlinear model, a nonlinear ARX model(NARX) is presented by Ohata, Furuta et.al. The NARX model consists of a set of ARX models with same orders at each output level. However, a systems order of a nonlinear system is different for each system state, usually. We propose new NARX model with variable order at a output levels. In addition, the proposed method is compared with the conventional NARX model by estimated accuracy. As a result, the conformance rate of the proposed method were larger than that by one of the NARX model. Furthermore, the mean and the variance of estimated error of the proposed method were smaller than one of the NARX model.</abstract><pub>IEEE</pub><doi>10.1109/IECON.2013.6699648</doi><tpages>6</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Accuracy Data models Equations Interpolation Mathematical model Nonlinear systems Predictive models |
| title | A identification method of a nonlinear ARX model with variable order for nonlinear systems |
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