摘要
经验模式分解(Empirical Mode Decomposition,简称EMD)是一种数据驱动的自适应非线性时变信号分解方法,可以把数据分解成具有物理意义的模式函数分量.采用EMD对极移序列进行分解,去除序列中的高频信号,然后基于最小二乘外推(Least Squares Extrapolation,简称LSE)和广义回归神经网络(General Regression Neural Network,简称GRNN)的组合模型对去除高频信号的极移序列进行1～10 d的超短期预报.实验结果表明:将该模型应用到极移超短期预报具有可行性,预报精度有明显改善.
Empirical mode decomposition （EMD） is used to analyze the nonlinear and time-varing signals. Being different from the traditional signal analysis methods, the decomposition is data-driven and self-adaptive. This paper applies EMD to decompose the polar motion （PM） series. Firstly, the high-frequency signals in the PM series are removed. Then the combined model of least squares extrapolation and general regression neural network is used to predict the PM series without the high-frequency signals from one to ten days in the future. The result shows the feasibility of this new model and obvious improvement of the prediction accuracy.
出处
《天文学报》
CSCD
北大核心
2012年第6期519-526,共8页
Acta Astronomica Sinica
基金
国家自然科学基金项目(U1231105
10878026)资助
关键词
天体测量
时间
方法
数据分析
astrometry, time, methods： data analysis
