利用端点延拓提高LS + NN模型的UT1-UTC预报精度
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摘要
现有UT1-UTC预报模式在进行周期项与残差项拟合分离时,通常没有考虑最小二乘拟合序列的端部效应,预报精度难以取得较大提高。针对最小二乘拟合存在的端部效应,首先采用灰色模型在UT1-UTC序列的两端进行数据延拓,形成一个新序列,然后对新序列进行最小二乘拟合,最后再联合最小二乘和神经网络(LS+NN)模型对UT1-UTC原始序列进行外推。结果表明,对UT1-UTC序列进行端点数据延拓再进行最小二乘拟合,能够有效地改善最小二乘拟合序列的端部效应;相对于常规LS+NN模型,端部效应改善的LS+NN模型的UT1-UTC预报精度有一定提高,尤其对中长期预报精度提高更为明显。
The prediction accuracy of UT1-UTC can be easily affected by the edge distortion of least-squares(LS) fitting time-series, referred to as the edge effect in data-processing fields, when periodic oscillations and residuals are separated by LS fitting. In order to alleviate the edge effect, the original UT1-UTC time-series is first extended on both boundaries using the GM(1, 1) grey model in this work. A LS extrapolation model is then set up using the extended time-series to remove the edge distortion to the boundaries. Finally, UT1-UTC predictions are generated by the combination of the edge effect correlated LS(ECLS) extrapolation model and extreme learning machine neural network(ECLS + NN). The numerical experiments show that the edge effect can be remarkably alleviated with the presented approach. In addition, the accuracy of the UT1-UTC short-term predictions by the ECLS + NN method is better than that obtained by the LS + NN approach, but only slighter. The medium-and long-term predictions, however, are noticeably more accurate than those by the LS + NN solution.
The prediction accuracy of UT1-UTC can be easily affected by the edge distortion of least-squares(LS) fitting time-series, referred to as the edge effect in data-processing fields, when periodic oscillations and residuals are separated by LS fitting. In order to alleviate the edge effect, the original UT1-UTC time-series is first extended on both boundaries using the GM(1, 1) grey model in this work. A LS extrapolation model is then set up using the extended time-series to remove the edge distortion to the boundaries. Finally, UT1-UTC predictions are generated by the combination of the edge effect correlated LS(ECLS) extrapolation model and extreme learning machine neural network(ECLS + NN). The numerical experiments show that the edge effect can be remarkably alleviated with the presented approach. In addition, the accuracy of the UT1-UTC short-term predictions by the ECLS + NN method is better than that obtained by the LS + NN approach, but only slighter. The medium-and long-term predictions, however, are noticeably more accurate than those by the LS + NN solution.
引文
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[10] PETIT G,LUZUM B.IERS conventions (2010) [R].Germany:Verlag des Bundesamts für Kartographie und Geodasie,2011:123-131.
[2] SCHUH H,ULRICH M,EGGER D,et al.Prediction of Earth orientation parameters by artificial neural networks[J].Journal of Geodesy,2002,76(5):247-258.
[3] XU X Q,ZHOU Y H.EOP prediction using least square fitting and autoregressive filter over optimized data intervals[J].Advances in Space Research,2015,56(10):2248-2253.
[4] 刘建,王琪洁,张昊.利用端部效应改正的LS + AR模型进行日长变化预报[J].武汉大学学报 (信息科学版),2013,38(8):916-919.
[5] 雷雨,蔡宏兵.顾及最小二乘拟合端点效应的日长变化预报[J].天文研究与技术,2016,13(4):441-445.
[6] LEI Y,GUO M,ZHAO D N,et al.Application of grey model GM (1,1) to ultra short-term predictions of universal time[J].Artificial Satellites,2016,51(1):19-29.
[7] 雷雨,赵丹宁,蔡宏兵.利用端部效应改善的最小二乘外推模型进行UT1-UTC预报[J].天文研究与技术,2018,15(3):302-307.
[8] HUANG G B,ZHU Q Y,SIEW C K.Extreme learning machine:theory and applications[J].Neurocomputing,2006,70(1/3):489-501.
[9] 雷雨,蔡宏兵,赵丹宁.样本输入方式对极端学习机预报日长变化的影响[J].天文研究与技术,2015,12(3):299-305.
[10] PETIT G,LUZUM B.IERS conventions (2010) [R].Germany:Verlag des Bundesamts für Kartographie und Geodasie,2011:123-131.