System identification by genetic algorithm
This paper presents a method for identifying systems through their input-output behavior and the Genetic Algorithm (GA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the origin...
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| creator | Duong, V. Stubberud, A.R. |
| description | This paper presents a method for identifying systems through their input-output behavior and the Genetic Algorithm (GA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function or linearly separable parameters. The results are compared to similar results from Least Squares (LS) identification methods. |
| doi_str_mv | 10.1109/AERO.2002.1035405 |
| format | Conference Proceeding |
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The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function or linearly separable parameters. The results are compared to similar results from Least Squares (LS) identification methods.</description><identifier>ISSN: 1095-323X</identifier><identifier>ISBN: 078037231X</identifier><identifier>ISBN: 9780780372313</identifier><identifier>DOI: 10.1109/AERO.2002.1035405</identifier><language>eng</language><publisher>IEEE</publisher><subject>Approximation error ; Genetic algorithms ; Least squares approximation ; Least squares methods ; Nonlinear systems ; Paper technology ; Parameter estimation ; Propulsion ; Stochastic systems ; System identification</subject><ispartof>2002 IEEE Aerospace Conference. Proceedings. 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The results are compared to similar results from Least Squares (LS) identification methods.</description><subject>Approximation error</subject><subject>Genetic algorithms</subject><subject>Least squares approximation</subject><subject>Least squares methods</subject><subject>Nonlinear systems</subject><subject>Paper technology</subject><subject>Parameter estimation</subject><subject>Propulsion</subject><subject>Stochastic systems</subject><subject>System identification</subject><issn>1095-323X</issn><isbn>078037231X</isbn><isbn>9780780372313</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2002</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkD1rwzAYhAVtoWmaH1C6eOpQsPtKsixpDCFpC4FAPyCbkeXXqYo_UksZ_O8rSG655eHuOEIeKGSUgn5Zrj92GQNgGQUuchBX5A6kAi4Zp_trMouQSDnj-1uy8P4XogQUmhYz8vw5-YBd4mrsg2ucNcENfVJNyQF7DM4mpj0Mows_3T25aUzrcXHxOfnerL9Wb-l29_q-Wm5TywQLKQcllWk0Mo1aa9XIvDaG5nFNzbRF0LFYFlgBpVYbUEI2BqpKWihYTms-J0_n3OM4_J3Qh7Jz3mLbmh6Hky-ZlLmILRF8PIMOEcvj6DozTuXlA_4PnctOiw</recordid><startdate>2002</startdate><enddate>2002</enddate><creator>Duong, V.</creator><creator>Stubberud, A.R.</creator><general>IEEE</general><scope>6IE</scope><scope>6IH</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIO</scope><scope>8FD</scope><scope>H8D</scope><scope>L7M</scope></search><sort><creationdate>2002</creationdate><title>System identification by genetic algorithm</title><author>Duong, V. ; Stubberud, A.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c252t-30878af9e29e9998f74daa14372d29ce0991676eb011c9a0857fa0bb7c06241d3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Approximation error</topic><topic>Genetic algorithms</topic><topic>Least squares approximation</topic><topic>Least squares methods</topic><topic>Nonlinear systems</topic><topic>Paper technology</topic><topic>Parameter estimation</topic><topic>Propulsion</topic><topic>Stochastic systems</topic><topic>System identification</topic><toplevel>online_resources</toplevel><creatorcontrib>Duong, V.</creatorcontrib><creatorcontrib>Stubberud, A.R.</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><collection>Technology Research Database</collection><collection>Aerospace Database</collection><collection>Advanced Technologies Database with Aerospace</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Duong, V.</au><au>Stubberud, A.R.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>System identification by genetic algorithm</atitle><btitle>2002 IEEE Aerospace Conference. Proceedings. Vol. 5</btitle><stitle>AERO</stitle><date>2002</date><risdate>2002</risdate><volume>5</volume><spage>5</spage><epage>5</epage><pages>5-5</pages><issn>1095-323X</issn><isbn>078037231X</isbn><isbn>9780780372313</isbn><abstract>This paper presents a method for identifying systems through their input-output behavior and the Genetic Algorithm (GA). The advantages of this technique are, first, it is not dependent on the deterministic or stochastic nature of the systems and, second, the globally optimized models for the original systems can be identified without the need of a differentiable measure function or linearly separable parameters. The results are compared to similar results from Least Squares (LS) identification methods.</abstract><pub>IEEE</pub><doi>10.1109/AERO.2002.1035405</doi><tpages>1</tpages></addata></record> |
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| ispartof | 2002 IEEE Aerospace Conference. Proceedings. Vol. 5, 2002, Vol.5, p.5-5 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation error Genetic algorithms Least squares approximation Least squares methods Nonlinear systems Paper technology Parameter estimation Propulsion Stochastic systems System identification |
| title | System identification by genetic algorithm |
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