Отрывок: The data which had significant gaps were not used in the training of the neural network. According to the results of paper [22], we determined the level of decomposition 3m  and obtained the representation of ionospheric parameter time series in the following form (see relation (1)):      k k3k3 3 tct2f )(,,  Information Technology and Nanotechnology – 2017 Data Science 1770 Fig. 2. Processing results of...
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dc.contributor.authorMandrikova, O.-
dc.contributor.authorPolozov, Yu.-
dc.contributor.authorGeppener, V.-
dc.date.accessioned2017-05-25 13:46:39-
dc.date.available2017-05-25 13:46:39-
dc.date.issued2017-
dc.identifierDspace\SGAU\20170523\64148ru
dc.identifier.citationMandrikova O. Method of ionospheric data analysis based on a combination of wavelet transform and neural networks / O. Mandrikova, Yu. Polozov, V. Geppener // Сборник трудов III международной конференции и молодежной школы «Информационные технологии и нанотехнологии» (ИТНТ-2017) - Самара: Новая техника, 2017. - С. 1767-1773.ru
dc.identifier.urihttp://repo.ssau.ru/handle/Informacionnye-tehnologii-i-nanotehnologii/Method-of-ionospheric-data-analysis-based-on-a-combination-of-wavelet-transform-and-neural-networks-64148-
dc.description.abstractThe paper presents a hybrid system based on a combination of wavelet filtering operations and regression neural networks. The system is adapted to analyze the ionosphere data obtained at "Paratunka" station (Kamchatka). Testing of the system has shown its efficiency in the tasks of analysis of characteristic properties of ionospheric data and detection of anomalies occurring during disturbed periods. For a detailed analysis of anomalies, computing solutions based on the application of continuous wavelet transform and threshold functions were suggested. The developed computational tools were implemented in software environment.ru
dc.description.sponsorshipThe research was supported by RSF Grant №14-11-00194.ru
dc.language.isoenru
dc.publisherНовая техникаru
dc.subjectwavelet-transformru
dc.subjectneural networksru
dc.subjectcritical frequency of the ionosphereru
dc.subjectionospheric stormsru
dc.subjectanomaliesru
dc.subjectmagnetic stormsru
dc.titleMethod of ionospheric data analysis based on a combination of wavelet transform and neural networksru
dc.typeArticleru
dc.textpartThe data which had significant gaps were not used in the training of the neural network. According to the results of paper [22], we determined the level of decomposition 3m  and obtained the representation of ionospheric parameter time series in the following form (see relation (1)):      k k3k3 3 tct2f )(,,  Information Technology and Nanotechnology – 2017 Data Science 1770 Fig. 2. Processing results of...-
Располагается в коллекциях: Информационные технологии и нанотехнологии

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