Convergence properties of an online learning algorithm in neural network models of complex systems
Asymptotic behavior of the online gradient algorithm with a constant step size employed for learning in neural network models of nonlinear systems having hidden layer are studied. The sufficient conditions guaranteeing the convergence of this algorithm in the random environment are established.
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| creator | Azarskov, V. N. Nikolaienko, S. A. Zhiteckii, L. S. |
| description | Asymptotic behavior of the online gradient algorithm with a constant step size employed for learning in neural network models of nonlinear systems having hidden layer are studied. The sufficient conditions guaranteeing the convergence of this algorithm in the random environment are established. |
| doi_str_mv | 10.1109/APUAVD.2013.6705293 |
| format | Conference Proceeding |
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S.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i175t-4a3bc14e1dc186da3dea3023ddb0615467f223f51ad7c755fb5ef023f9cea7733</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Artificial neural networks</topic><topic>Biological neural networks</topic><topic>Conferences</topic><topic>Convergence</topic><topic>gradient algorithm</topic><topic>learning</topic><topic>neural network</topic><topic>Neurons</topic><topic>nonlinear model</topic><topic>Unmanned aerial vehicles</topic><toplevel>online_resources</toplevel><creatorcontrib>Azarskov, V. N.</creatorcontrib><creatorcontrib>Nikolaienko, S. A.</creatorcontrib><creatorcontrib>Zhiteckii, L. 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S.</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Convergence properties of an online learning algorithm in neural network models of complex systems</atitle><btitle>2013 IEEE 2nd International Conference Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD)</btitle><stitle>APUAVD</stitle><date>2013-10</date><risdate>2013</risdate><spage>89</spage><epage>92</epage><pages>89-92</pages><isbn>1479933058</isbn><isbn>9781479933051</isbn><eisbn>9781479933068</eisbn><eisbn>1479933066</eisbn><abstract>Asymptotic behavior of the online gradient algorithm with a constant step size employed for learning in neural network models of nonlinear systems having hidden layer are studied. The sufficient conditions guaranteeing the convergence of this algorithm in the random environment are established.</abstract><pub>IEEE</pub><doi>10.1109/APUAVD.2013.6705293</doi><tpages>4</tpages></addata></record> |
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| ispartof | 2013 IEEE 2nd International Conference Actual Problems of Unmanned Air Vehicles Developments Proceedings (APUAVD), 2013, p.89-92 |
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| language | eng |
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| source | IEEE Electronic Library (IEL) Conference Proceedings |
| subjects | Artificial neural networks Biological neural networks Conferences Convergence gradient algorithm learning neural network Neurons nonlinear model Unmanned aerial vehicles |
| title | Convergence properties of an online learning algorithm in neural network models of complex systems |
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