NTSC-IR
Short-term prediction of UT1-UTC by combination of the grey model and neural networks
Lei, Yu1,2; Guo, Min3; Hu, Dan-dan3; Cai, Hong-bing1,2; Zhao, Dan-ning1,4; Hu, Zhao-peng1,4; Gao, Yu-ping1,2
2017-01-15
Source PublicationADVANCES IN SPACE RESEARCH
ISSN0273-1177
Volume59Issue:2Pages:524-531
AbstractUT1-UTC predictions especially short-term predictions are essential in various fields linked to reference systems such as space navigation and precise orbit determinations of artificial Earth satellites. In this paper, an integrated model combining the grey model GM (1, 1) and neural networks (NN) are proposed for predicting UT1-UTC. In this approach, the effects of the Solid Earth tides and ocean tides together with leap seconds are first removed from observed UT1-UTC data to derive UT1R-TAI. Next the derived UT1R-TAI time-series are de-trended using the GM(1, 1) and then residuals are obtained. Then the residuals are used to train a network. The subsequently predicted residuals are added to the GM(1, 1) to obtain the UT1R-TAI predictions. Finally, the predicted UT1R-TAI are corrected for the tides together with leap seconds to obtain UT1-UTC predictions. The daily values of UT1-UTC between January 7, 2010 and August 6, 2016 from the International Earth Rotation and Reference Systems Service (IERS) 08 C04 series are used for modeling and validation of the proposed model. The results of the predictions up to 30 days in the future are analyzed and compared with those by the GM(1, 1)-only model and combination of the least-squares (LS) extrapolation of the harmonic model including the linear part, annual and semi-annual oscillations and NN. It is found that the proposed model outperforms the other two solutions. In addition, the predictions are compared with those from the Earth Orientation Parameters Prediction Comparison Campaign (EOP PCC) lasting from October 1, 2005 to February 28, 2008. The results show that the prediction accuracy is inferior to that of those methods taking into account atmospheric angular momentum (AAM), i.e., Kalman filter and adaptive transform from AAM to LODR, but noticeably better that of the other existing methods and techniques, e.g., autoregressive filtering and least-squares collocation. (C) 2016 COSPAR. Published by Elsevier Ltd. All rights reserved.
KeywordUT1-UTC Prediction Grey model Neural Networks (NN)
Funding OrganizationWest Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences
DOI10.1016/j.asr.2016.10.030
WOS KeywordEARTH ORIENTATION PARAMETERS ; ATMOSPHERIC ANGULAR-MOMENTUM ; EXTREME LEARNING-MACHINE ; LEAST-SQUARES ; FILTER
Language英语
Funding ProjectWest Light Foundation of Chinese Academy of Sciences
Funding OrganizationWest Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences ; West Light Foundation of Chinese Academy of Sciences
WOS Research AreaAstronomy & Astrophysics ; Geology ; Meteorology & Atmospheric Sciences
WOS SubjectAstronomy & Astrophysics ; Geosciences, Multidisciplinary ; Meteorology & Atmospheric Sciences
WOS IDWOS:000392773700003
PublisherELSEVIER SCI LTD
Citation statistics
Document Type期刊论文
Identifierhttp://210.72.145.45/handle/361003/11343
Collection中国科学院国家授时中心
Corresponding AuthorLei, Yu
Affiliation1.Chinese Acad Sci, Natl Time Serv Ctr, Xian 710600, Shaanxi, Peoples R China
2.Chinese Acad Sci, Key Lab Time & Frequency Primary Stand, Xian 710600, Shaanxi, Peoples R China
3.Xian Inst Opt & Precis Mech, Xian 710119, Shaanxi, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
Recommended Citation
GB/T 7714
Lei, Yu,Guo, Min,Hu, Dan-dan,et al. Short-term prediction of UT1-UTC by combination of the grey model and neural networks[J]. ADVANCES IN SPACE RESEARCH,2017,59(2):524-531.
APA Lei, Yu.,Guo, Min.,Hu, Dan-dan.,Cai, Hong-bing.,Zhao, Dan-ning.,...&Gao, Yu-ping.(2017).Short-term prediction of UT1-UTC by combination of the grey model and neural networks.ADVANCES IN SPACE RESEARCH,59(2),524-531.
MLA Lei, Yu,et al."Short-term prediction of UT1-UTC by combination of the grey model and neural networks".ADVANCES IN SPACE RESEARCH 59.2(2017):524-531.
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