Accumulative Learning using Multiple ANN for Flexible Link Control
This paper presents a scheme of multiple neural networks (MNNs) with a new strategy of combination. This combination can obtain an accumulative learning: the knowledge is increased by gradually adding more neural networks to the system. This scheme is applied to flexible link control via feedback-er...
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| Veröffentlicht in: | IEEE transactions on aerospace and electronic systems 2010-04, Vol.46 (2), p.508-524 |
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| container_title | IEEE transactions on aerospace and electronic systems |
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| creator | De Almeida Neto, Areolino Goes, Luis Carlos Sandoval Nascimento, Cairo Lucio |
| description | This paper presents a scheme of multiple neural networks (MNNs) with a new strategy of combination. This combination can obtain an accumulative learning: the knowledge is increased by gradually adding more neural networks to the system. This scheme is applied to flexible link control via feedback-error-learning (FEL) strategy, here called multi-network-feedback-error-learning. Three different neural control approaches are used to control a flexible link, and it is shown that a better inverse dynamic model of the plant is obtained in this case. |
| doi_str_mv | 10.1109/TAES.2010.5461638 |
| format | Article |
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Three different neural control approaches are used to control a flexible link, and it is shown that a better inverse dynamic model of the plant is obtained in this case.</description><identifier>ISSN: 0018-9251</identifier><identifier>EISSN: 1557-9603</identifier><identifier>DOI: 10.1109/TAES.2010.5461638</identifier><identifier>CODEN: IEARAX</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Aerodynamics ; Aircraft components ; Artificial neural networks ; Control nonlinearities ; Control systems ; Dynamical systems ; Electronic systems ; Error correction ; Inverse dynamics ; Inverse problems ; Learning ; Learning theory ; Links ; Neural networks ; Nonlinear control systems ; Nonlinear dynamical systems ; Space technology ; Strategy</subject><ispartof>IEEE transactions on aerospace and electronic systems, 2010-04, Vol.46 (2), p.508-524</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. 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Three different neural control approaches are used to control a flexible link, and it is shown that a better inverse dynamic model of the plant is obtained in this case.</description><subject>Aerodynamics</subject><subject>Aircraft components</subject><subject>Artificial neural networks</subject><subject>Control nonlinearities</subject><subject>Control systems</subject><subject>Dynamical systems</subject><subject>Electronic systems</subject><subject>Error correction</subject><subject>Inverse dynamics</subject><subject>Inverse problems</subject><subject>Learning</subject><subject>Learning theory</subject><subject>Links</subject><subject>Neural networks</subject><subject>Nonlinear control systems</subject><subject>Nonlinear dynamical systems</subject><subject>Space technology</subject><subject>Strategy</subject><issn>0018-9251</issn><issn>1557-9603</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2010</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNp9kE1PwzAMhiMEEmPwAxCXShzg0hE3nz2WaQOkMQ6Mc5RlKcro2pG0CP49qTY4cOBi67UfW_aL0DngEQDObxbF5HmU4SgZ5cCJPEADYEykOcfkEA0wBpnmGYNjdBLCOkoqKRmg28KYbtNVunUfNplZ7WtXvyZd6ONjV7VuW9mkmM-TsvHJtLKfbhkLM1e_JeOmbn1TnaKjUlfBnu3zEL1MJ4vxfTp7unsYF7PUECbbFLS0Wqwo5zzTlGrBM6qtoLDKMV0aw3gWT-ekxBJrzgzVfSePumRSSE2G6Gq3d-ub986GVm1cMLaqdG2bLijJGJeSCR7J639J4AKIEJCRiF7-QddN5-v4hwKcCRCcgYgU7CjjmxC8LdXWu432XxFSvf-q91_1_qu9_3HmYjfjrLW__E_3G_-tfko</recordid><startdate>201004</startdate><enddate>201004</enddate><creator>De Almeida Neto, Areolino</creator><creator>Goes, Luis Carlos Sandoval</creator><creator>Nascimento, Cairo Lucio</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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| language | eng |
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| source | IEEE Electronic Library (IEL) |
| subjects | Aerodynamics Aircraft components Artificial neural networks Control nonlinearities Control systems Dynamical systems Electronic systems Error correction Inverse dynamics Inverse problems Learning Learning theory Links Neural networks Nonlinear control systems Nonlinear dynamical systems Space technology Strategy |
| title | Accumulative Learning using Multiple ANN for Flexible Link Control |
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