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Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2017, Issue 2, Pages 54–61 (Mi itvs265)  

INTELLIGENT SYSTEMS

Teaching big hybrid neural networks for time series prediction

D. Y. Nagornykhab

a Novosibirsk State University
b A.P. Ershov Institute of Informatics Systems, Siberian Branch of the Russian Academy of Sciences
Abstract: The article describes hybrid approach in neural networks, used in prediction of time series, as well as the specific aspects of teaching hybrid neural networks consisting of Self-Organizing Maps (SOM) and Multilayer Perсeptron (MLP). Also, paper contains the results, gained during the process of building and teaching the large hybrid neural network and the new algorithm of equitable teaching for self-organizing layer.
Keywords: neural nets, hybrid neural nets, self-organizing maps, time series prediction, function approximation.
Document Type: Article
Language: Russian
Citation: D. Y. Nagornykh, “Teaching big hybrid neural networks for time series prediction”, Informatsionnye Tekhnologii i Vychslitel'nye Sistemy, 2017, no. 2, 54–61
Citation in format AMSBIB
\Bibitem{Nag17}
\by D.~Y.~Nagornykh
\paper Teaching big hybrid neural networks for time series prediction
\jour Informatsionnye Tekhnologii i Vychslitel'nye Sistemy
\yr 2017
\issue 2
\pages 54--61
\mathnet{http://mi.mathnet.ru/itvs265}
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