1. Репозиторий Самарского национального исследовательского университета имени академика С.П. Королёва
  2. 04. Материалы конференций
  3. Информационные технологии и нанотехнологии
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dc.date2020
dc.date.accessioned2025-08-22T12:18:27Z-
dc.date.available2025-08-22T12:18:27Z-
dc.date.issued2020
dc.identifier.identifierDspace\SGAU\20200730\84860
dc.identifier.citationRozenberg Y.N. Some approaches of improving the quality of artificial neural network training / Y.N. Rozenberg, A.М. Olshansky, I.А. Dovgerd, G.А. Dovgerd, A.V. Ignatenkov, P.V. Ignatenkov // Информационные технологии и нанотехнологии (ИТНТ-2020). Сборник трудов по материалам VI Международной конференции и молодежной школы (г. Самара, 26-29 мая): в 4 т. / Самар. нац.-исслед. ун-т им. С. П. Королева (Самар. ун-т), Ин-т систем. обраб. изобр. РАН-фил. ФНИЦ "Кристаллография и фотоника" РАН; [под ред. В. А. Фурсова]. – Самара: Изд-во Самар. ун-та, 2020. – Том 4. Науки о данных. – 2020. – С. 240-242.
dc.identifier.urihttp://repo.ssau.ru/jspui/handle/123456789/12638-
dc.description.abstractThe paper is devoted to a problem of regular improving the quality of artificial neural network (ANN) training. The object of study is a complex neural network which consists of 2-dimensional Kohonen network and Wilshaw and von der Malsburg network. These networks are applied to a timetable problem for transport systems. The main existing results of using optimal control theory for ANN training are analyzed; authors suggest a new technique based on the direct neural control. Authors give comparative values of error during a training process for the traditional methods and a new approach. It is presented that this new technique is better than the traditional one for considered neural networks.
dc.languageen_US
dc.titleSome approaches of improving the quality of artificial neural network training
dc.typeArticle
local.identifier.oldurihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Some-approaches-of-improving-the-quality-of-artificial-neural-network-training-84860
local.identifier.oldurihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Some-approaches-of-improving-the-quality-of-artificial-neural-network-training-84860
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