Please use this identifier to cite or link to this item: https://er.nau.edu.ua/handle/NAU/28359
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dc.contributor.authorV.N.Azarskov-
dc.contributor.authorL.S. Zhiteckii-
dc.contributor.authorS. A. Nikolaienko-
dc.date.accessioned2017-05-26T11:08:38Z-
dc.date.available2017-05-26T11:08:38Z-
dc.date.issued2013-10-
dc.identifier.urihttp://er.nau.edu.ua/handle/NAU/28359-
dc.description.abstractAsymptotic 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 establisheduk_UA
dc.language.isoenuk_UA
dc.publisherВД "Освіта України"uk_UA
dc.subjectnonlinear model; neural network; gradient algorithm; learning; convergenceuk_UA
dc.titleConvergence Properties of an Online Learning Algorithm in Neural Network Models of Complex Systemsuk_UA
dc.typeArticleuk_UA
Appears in Collections:Матеріали конференцій кафедри аерокосмічних систем управління

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