innosferaНаука и инновацииScience and Innovations1818-98572412-9372Издательский дом «Белорусская наука»innosfera-228Research ArticleИСКУССТВЕННЫЙ ИНТЕЛЛЕКТПрогнозирование временных рядов телеметрии космических аппаратов на основе ансамблей нейронных сетейForecasting time series of spacecraft telemetry based on ensembles of neural networksДудкинА.DoudkinA.

завлабораторией идентификации систем, доктор технических наук, профессор

МарушкоЕ.MarushkoY.

научный сотрудник лаборатории идентификации систем, магистр

ГанченкоВ.GanchenkoV.

ст. научный сотрудник лаборатории идентификации систем, кандидат технических наук

Объединенный институт проблем информатики НАН БеларусиBelarus202122122022051622Copyright © Издательский дом «Белорусская наука», 20222022Издательский дом «Белорусская наука»Издательский дом «Белорусская наука»https://innosfera.belnauka.by/jour/about/submissions#copyrightNoticehttps://innosfera.belnauka.by/jour/article/view/228

Описывается двухуровневая модель ансамблей нейронных сетей для прогнозирования многомерных временных рядов телеметрии подсистем космических аппаратов, которая реализована в системе идентификации их состояния по телеметрическим данным для наземного командно-измерительного комплекса

The article describes a two-level model of neural network ensembles for predicting multidimensional telemetry time series of spacecraft subsystems. The developed model is implemented in the system for identifying their state by telemetry data for the ground command-measuring complex.

ReferencesShort-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals / Hao Quan [et al.] // IEEE Transactions on Neural Networks and Learning Systems. – Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals. – 2013. Vol. 25. Iss. 2. ISSN: 2162–237X. P. 303–315.Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals / Hao Quan [et al.] // IEEE Transactions on Neural Networks and Learning Systems. – Short-Term Load and Wind Power Forecasting Using Neural Network-Based Prediction Intervals. – 2013. Vol. 25. Iss. 2. ISSN: 2162–237X. P. 303–315.Lysayak A. S. Prediction of multidimensional time series / A. S. Lysayak, B. Y. Ryabko // Vestnik SibGUTI. 2014. №4. P. 75–88.Lysayak A. S. Prediction of multidimensional time series / A. S. Lysayak, B. Y. Ryabko // Vestnik SibGUTI. 2014. №4. P. 75–88.Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir / M. Valipour [et al.] // Journal of Hydrology 476 (2013). P. 433–441.Comparison of the ARMA, ARIMA, and the autoregressive artificial neural network models in forecasting the monthly inflow of Dez dam reservoir / M. Valipour [et al.] // Journal of Hydrology 476 (2013). P. 433–441.Khachumov V. M. Review of Standards and the concept of monitoring, control and diagnostics of the spacecraft tools building / V. M. Khachumov [et al.] // Software Systems: Theory and Applications. №3(26). Vol. 6. 2015. P. 21–43.Khachumov V. M. Review of Standards and the concept of monitoring, control and diagnostics of the spacecraft tools building / V. M. Khachumov [et al.] // Software Systems: Theory and Applications. №3(26). Vol. 6. 2015. P. 21–43.Emelyanov Yu.G.: Neural orientation angles and distance of the spacecraft sensor control system / Yu. G. Emelyanov, K. A. Konstantinov, S. V. Pogodin, etc. // Software Systems: Theory and Applications. 2010. №1(1). P. 45–59.Emelyanov Yu.G.: Neural orientation angles and distance of the spacecraft sensor control system / Yu. G. Emelyanov, K. A. Konstantinov, S. V. Pogodin, etc. // Software Systems: Theory and Applications. 2010. №1(1). P. 45–59.Neural network ensemble operators for time series forecasting / Nikolaos Kourentzes [et al.] // Expert Systems with Applications. July 2014. Vol.41. Iss. 9. ISSN: 0957–4174. P. 4235–4244.Neural network ensemble operators for time series forecasting / Nikolaos Kourentzes [et al.] // Expert Systems with Applications. July 2014. Vol.41. Iss. 9. ISSN: 0957–4174. P. 4235–4244.

The authors declare that there are no conflicts of interest present.