Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network
A model-based dynamic recurrent neural network (MBDRNN) is used in this paper to improve the linearized model of a simple inverted pendulum (SIP). The MBDRNN's equations start as those of the linearized SIP model. Then, through back-propagation-based training, the MBDRNN's activation funct...
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| creator | Karam, M. Zohdy, M.A. |
| description | A model-based dynamic recurrent neural network (MBDRNN) is used in this paper to improve the linearized model of a simple inverted pendulum (SIP). The MBDRNN's equations start as those of the linearized SIP model. Then, through back-propagation-based training, the MBDRNN's activation functions' weights are modified with the objective of improving the linearized SIP model. Simulation results show that the MBDRRN effectively improved the linearized model. By tuning several of the MBDRNN parameters, an improved configuration was found yielding a satisfactory' small modeling approximation error. |
| doi_str_mv | 10.1109/SSST.2005.1460881 |
| format | Conference Proceeding |
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By tuning several of the MBDRNN parameters, an improved configuration was found yielding a satisfactory' small modeling approximation error.</description><subject>Approximation error</subject><subject>Computer networks</subject><subject>Mean square error methods</subject><subject>Modeling</subject><subject>Neural networks</subject><subject>Nonlinear dynamical systems</subject><subject>Nonlinear equations</subject><subject>Nonlinear systems</subject><subject>Recurrent neural networks</subject><subject>Systems engineering and theory</subject><issn>0094-2898</issn><issn>2161-8135</issn><isbn>0780388089</isbn><isbn>9780780388086</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2005</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><sourceid>RIE</sourceid><recordid>eNotkMtOwzAURC0eEmnhAxAb_0DC9Su2l6iCglTEokViVznxNTIkaeUkoP49Qe1qFnM00hlCbhkUjIG9X6_Xm4IDqILJEoxhZyTjrGS5YUKdkxloA8IYMPaCZABW5txYc0Vmff8FAGXJVUY-Xncem9h9Ukf72O4bpLH7wTSgp3vs_NiMLR37I9D-s3nl-qn0h861saYJ6zEl7Aba4ZhcM8Xwu0vf1-QyuKbHm1POyfvT42bxnK_eli-Lh1UemVZDzq2soMLaeuBBGScqXWolpQ_cheBrFFbWwhurg9CKS1Ea9NWkqQOr7CQ4J3fH3YiI232KrUuH7ekR8QdyE1RX</recordid><startdate>2005</startdate><enddate>2005</enddate><creator>Karam, M.</creator><creator>Zohdy, M.A.</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>2005</creationdate><title>Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network</title><author>Karam, M. ; Zohdy, M.A.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-294b0bec9d02f58a3b767544df2affdce394c3d897f37524368edb2167f1b9803</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2005</creationdate><topic>Approximation error</topic><topic>Computer networks</topic><topic>Mean square error methods</topic><topic>Modeling</topic><topic>Neural networks</topic><topic>Nonlinear dynamical systems</topic><topic>Nonlinear equations</topic><topic>Nonlinear systems</topic><topic>Recurrent neural networks</topic><topic>Systems engineering and theory</topic><toplevel>online_resources</toplevel><creatorcontrib>Karam, M.</creatorcontrib><creatorcontrib>Zohdy, M.A.</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>Karam, M.</au><au>Zohdy, M.A.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network</atitle><btitle>Proceedings of the Thirty-Seventh Southeastern Symposium on System Theory, 2005. SSST '05</btitle><stitle>SSST</stitle><date>2005</date><risdate>2005</risdate><spage>78</spage><epage>82</epage><pages>78-82</pages><issn>0094-2898</issn><eissn>2161-8135</eissn><isbn>0780388089</isbn><isbn>9780780388086</isbn><abstract>A model-based dynamic recurrent neural network (MBDRNN) is used in this paper to improve the linearized model of a simple inverted pendulum (SIP). The MBDRNN's equations start as those of the linearized SIP model. Then, through back-propagation-based training, the MBDRNN's activation functions' weights are modified with the objective of improving the linearized SIP model. Simulation results show that the MBDRRN effectively improved the linearized model. By tuning several of the MBDRNN parameters, an improved configuration was found yielding a satisfactory' small modeling approximation error.</abstract><pub>IEEE</pub><doi>10.1109/SSST.2005.1460881</doi><tpages>5</tpages></addata></record> |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Approximation error Computer networks Mean square error methods Modeling Neural networks Nonlinear dynamical systems Nonlinear equations Nonlinear systems Recurrent neural networks Systems engineering and theory |
| title | Modeling a simple inverted pendulum using a model-based dynamic recurrent neural network |
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