BP神经网络在极移预报中的应用

Application of BP neural network model in prediction of polar motion

为了提高地球定向参数极移的预报精度,建立了一个极移数据预报模型。利用傅里叶分析研究插值基础序列的周期特性,验证了基础序列重采样的可行性,提取插值基础序列数据的趋势项,利用多输入-单输出反向传播(Back Propagation,BP)神经网络建模预报不同跨度的残差序列,合并趋势项和残差序列得到最终的极移预报。预报结果表明,选取合适的插值基础序列得到的预报极移精度较高,此BP神经网络能够有效地应用于地球定向参数极移的预报。

A predictive model was set up to improve the prediction precision of polar motion of earth orientation parameters. The periodic characteristics of interpolated basic series was studied by Fourier analysis, the feasibility of basic series resampling was verified, then the trend terms were derived from the interpolation basic series, and the multiple input-single output BP（ Back Propagation） neural network model was used to predict the residual series for different time spans. Finally the predicted polar motion was achieved by combining the trend terms with residual series. Prediction results indicate that the appropriate selection of interpolation basic series can realize high precision prediction of polar motion. Moreover, the BP neural network can be applied to the prediction of polar motion of earth orientation parameters effectively.