Application of the radial basis function neural network to the short term prediction of the Earth's polar motion

doi:10.1007/s11200-017-0805-4

CORC > 上海天文台 > 中国科学院上海天文台

	Application of the radial basis function neural network to the short term prediction of the Earth's polar motion
	Wang, Guocheng 3; Liu, Lintao 3; Tu, Yi 4; Xu, Xueqing 1; Yuan, Yunbin 3; Song, Min 3; Li, Wenping 2
刊名	STUDIA GEOPHYSICA ET GEODAETICA
	2018-04-01
卷号	62 期号:2 页码:243-254
关键词	Rbfnn Model Short-term Forecast Ultra-short-term Prediction Polar Motion
ISSN号	0039-3169
DOI	10.1007/s11200-017-0805-4
英文摘要	By a number of test cases using different sample numbers and sample lengths, we obtain a Radial Basis Function Neural Network (RBFNN) model that is suitable for the short-term forecast of polar motion, especially for the ultra-short-term forecast. By using the same data sample of Earth's polar motion, this RBFNN model can achieve better short-term prediction accuracy than the least-squares+autoregressive (LS+AR) method, and better ultra-short-term prediction accuracy than the LS+AR+Kalman method. Using this model to forecast the polar motion data from January 1, 2002 to December 30, 2007 and from January 1, 2010 to December 30, 2016, respectively, experimental results show that the ultra-short-term forecast accuracy of this RBFNN model is within a precision of 3.15 and 3.08 milliseconds of arc (mas) in polar motion x direction, 2.02 and 2.04 mas in polar motion y direction; the short-term forecast accuracy of RBFNN model is within a precision of 8.83 and 8.69 mas in polar motion x direction, and 5.59 and 5.85 mas in polar motion y direction. As is stated above, this RBFNN model is well capable of forecasting the short-term of polar motion, especially the ultra-short-term.
WOS研究方向	Geochemistry & Geophysics
语种	英语
出版者	SPRINGER
WOS记录号	WOS:000433209100004
内容类型	期刊论文
源URL	[http://119.78.226.72/handle/331011/32287]
专题	中国科学院上海天文台
通讯作者	Wang, Guocheng
作者单位	1.Chinese Acad Sci, Shanghai Astron Observ, Shanghai 200030, Peoples R China 2.Hunan Inst Technol, Hengyang 421002, Peoples R China 3.Chinese Acad Sci, Inst Geodesy & Geophys, State Key Lab Geodesy & Earths Dynam, Wuhan 430077, Hubei, Peoples R China 4.China Three Gorges Univ, Minist Educ, Three Gorges Reservoir Area, Key Lab Geol Hazards, Yichang 443002, Peoples R China
推荐引用方式 GB/T 7714	Wang, Guocheng,Liu, Lintao,Tu, Yi,et al. Application of the radial basis function neural network to the short term prediction of the Earth's polar motion[J]. STUDIA GEOPHYSICA ET GEODAETICA,2018,62(2):243-254.
APA	Wang, Guocheng.,Liu, Lintao.,Tu, Yi.,Xu, Xueqing.,Yuan, Yunbin.,...&Li, Wenping.(2018).Application of the radial basis function neural network to the short term prediction of the Earth's polar motion.STUDIA GEOPHYSICA ET GEODAETICA,62(2),243-254.
MLA	Wang, Guocheng,et al."Application of the radial basis function neural network to the short term prediction of the Earth's polar motion".STUDIA GEOPHYSICA ET GEODAETICA 62.2(2018):243-254.