不同组合模型的地球自转参数预报对比

Comparison of Earth Rotation Parameters Prediction Based on Different Combination Models

以最小二乘法、小波去噪、小波神经网络和BP神经网络为基础,构建9种地球自转参数预报模型,并进行30 d短周期预报。结果表明,对于极移预报,基于BP神经网络的地球自转参数预报模型效果不佳,RMSE均大于1.5 mas;最小二乘与小波神经网络组合模型的预报效果最好,RMSE小于1.3 mas。对于日长变化预报,最小二乘与小波神经网络组合模型的预报效果不佳,RMSE均大于0.18 ms;小波神经网络预报模型预报效果最好,RMSE为0.07 ms。

Based on least squares method,wavelet denoising,wavelet neural network and BP neural network,we construct nine kinds of prediction models of Earth rotation parameters.The results show that the prediction of Earth rotation parameters based on BP neural network is not effective,and the RMSE is greater than 1.5 mas.The least squares and wavelet neural network combined model has the best prediction effect,and the RMSE is less than 1.3 mas.For the prediction of length of day,the combined model of least squares and wavelet neural network is not good,and the RMSE is greater than 0.18 ms.The RMSE of wavelet neural network prediction model is the smallest,which is 0.07 ms.The results show that the wavelet neural network prediction model has the best prediction effect on the length of day.