Identification of linear discrete time systems using linear recurrent neural networks
This paper considers the development of a neural time identification scheme for unknown linear dynamical systems using linear artificial neural networks. The neural networks model used in this paper is a linear recurrent model. The proposed identification scheme is based on minimization of the least...
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| creator | Sebakhy, O.A. Kader, H.M.A. Youssef, W.A. Deghiedi, S. |
| description | This paper considers the development of a neural time identification scheme for unknown linear dynamical systems using linear artificial neural networks. The neural networks model used in this paper is a linear recurrent model. The proposed identification scheme is based on minimization of the least squares errors between the actual and the estimated parameters. The analysis and design of this system are discussed. The operating characteristics of the proposed recurrent neural networks for system identification are demonstrated via an example. |
| doi_str_mv | 10.1109/ISIE.1996.548450 |
| format | Conference Proceeding |
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The neural networks model used in this paper is a linear recurrent model. The proposed identification scheme is based on minimization of the least squares errors between the actual and the estimated parameters. The analysis and design of this system are discussed. The operating characteristics of the proposed recurrent neural networks for system identification are demonstrated via an example.</description><identifier>ISBN: 0780333349</identifier><identifier>ISBN: 9780780333345</identifier><identifier>DOI: 10.1109/ISIE.1996.548450</identifier><language>eng</language><publisher>IEEE</publisher><subject>Discrete time systems ; Equations ; Least squares approximation ; Parameter estimation ; Recurrent neural networks ; Signal processing ; State estimation ; Time measurement ; Vectors ; Yield estimation</subject><ispartof>Proceedings of IEEE International Symposium on Industrial Electronics, 1996, Vol.1, p.374-379 vol.1</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/548450$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,776,780,785,786,2052,4036,4037,27902,54895</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/548450$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Sebakhy, O.A.</creatorcontrib><creatorcontrib>Kader, H.M.A.</creatorcontrib><creatorcontrib>Youssef, W.A.</creatorcontrib><creatorcontrib>Deghiedi, S.</creatorcontrib><title>Identification of linear discrete time systems using linear recurrent neural networks</title><title>Proceedings of IEEE International Symposium on Industrial Electronics</title><addtitle>ISIE</addtitle><description>This paper considers the development of a neural time identification scheme for unknown linear dynamical systems using linear artificial neural networks. The neural networks model used in this paper is a linear recurrent model. The proposed identification scheme is based on minimization of the least squares errors between the actual and the estimated parameters. The analysis and design of this system are discussed. The operating characteristics of the proposed recurrent neural networks for system identification are demonstrated via an example.</description><subject>Discrete time systems</subject><subject>Equations</subject><subject>Least squares approximation</subject><subject>Parameter estimation</subject><subject>Recurrent neural networks</subject><subject>Signal processing</subject><subject>State estimation</subject><subject>Time measurement</subject><subject>Vectors</subject><subject>Yield estimation</subject><isbn>0780333349</isbn><isbn>9780780333345</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>1996</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNo1T81KxDAYDIigrnsXT3mB1nz5aZOjLKsWFjzonpc0_SLRtitJiuzbG1idywzDzMAQcgesBmDmoXvrtjUY09RKaqnYBblhrWaiQJorsk7pkxVIBZzBNdl3A845-OBsDseZHj0dw4w20iEkFzEjzWFCmk4p45ToksL88R-J6JYYS5_OuEQ7Fso_x_iVbsmlt2PC9R-vyP5p-755qXavz93mcVcFYDJXulV9r4TsjVSoHOda6MFiA63ljUeunUADjvuiGrBGta2TyhdHKSbAihW5P-8GRDx8xzDZeDqcj4tfOG1Peg</recordid><startdate>1996</startdate><enddate>1996</enddate><creator>Sebakhy, O.A.</creator><creator>Kader, H.M.A.</creator><creator>Youssef, W.A.</creator><creator>Deghiedi, S.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>1996</creationdate><title>Identification of linear discrete time systems using linear recurrent neural networks</title><author>Sebakhy, O.A. ; Kader, H.M.A. ; Youssef, W.A. ; Deghiedi, S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i104t-875bb534b945e5c22838dae617a26fe28c3e91c2f28c61a9577c45f1c255031a3</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>1996</creationdate><topic>Discrete time systems</topic><topic>Equations</topic><topic>Least squares approximation</topic><topic>Parameter estimation</topic><topic>Recurrent neural networks</topic><topic>Signal processing</topic><topic>State estimation</topic><topic>Time measurement</topic><topic>Vectors</topic><topic>Yield estimation</topic><toplevel>online_resources</toplevel><creatorcontrib>Sebakhy, O.A.</creatorcontrib><creatorcontrib>Kader, H.M.A.</creatorcontrib><creatorcontrib>Youssef, W.A.</creatorcontrib><creatorcontrib>Deghiedi, S.</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>Sebakhy, O.A.</au><au>Kader, H.M.A.</au><au>Youssef, W.A.</au><au>Deghiedi, S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Identification of linear discrete time systems using linear recurrent neural networks</atitle><btitle>Proceedings of IEEE International Symposium on Industrial Electronics</btitle><stitle>ISIE</stitle><date>1996</date><risdate>1996</risdate><volume>1</volume><spage>374</spage><epage>379 vol.1</epage><pages>374-379 vol.1</pages><isbn>0780333349</isbn><isbn>9780780333345</isbn><abstract>This paper considers the development of a neural time identification scheme for unknown linear dynamical systems using linear artificial neural networks. The neural networks model used in this paper is a linear recurrent model. The proposed identification scheme is based on minimization of the least squares errors between the actual and the estimated parameters. The analysis and design of this system are discussed. The operating characteristics of the proposed recurrent neural networks for system identification are demonstrated via an example.</abstract><pub>IEEE</pub><doi>10.1109/ISIE.1996.548450</doi></addata></record> |
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| identifier | ISBN: 0780333349 |
| ispartof | Proceedings of IEEE International Symposium on Industrial Electronics, 1996, Vol.1, p.374-379 vol.1 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Discrete time systems Equations Least squares approximation Parameter estimation Recurrent neural networks Signal processing State estimation Time measurement Vectors Yield estimation |
| title | Identification of linear discrete time systems using linear recurrent neural networks |
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