基于小波变换与RBF神经网络的GNSS水汽值预测研究

利用小波变换与RBF神经网络方法预测河北省GNSS水汽值。首先对GNSS测站水汽序列进行小波分解，然后利用RBF神经网络对小波分解的高频与低频信号进行预测，最后通过实验选择合适的高频与低频信号结果重构获得GNSS水汽值预测值。以实测GNSS水汽值为标准，基于小波变换与RBF神经网络预测的GNSS水汽值精度高于单一RBF神经网络预测精度，但预测结果的精度随着预测时长的增加而降低。

The Study of GNSS-PWV Prediction Based on Wavelet Transform and RBF Neural Network

This paper takes Hebei province as the research area, using wavelet transform and RBF neural network methods to carry out GNSS-PWV prediction research. Firstly, wavelet decomposition is performed on the PWV sequence of GNSS stations, and then the high and low frequency signals decomposed by the wavelet are predicted by the RBF neural network. Finally, the appropriate high frequency and low frequency signals are selected through experiments to reconstruct the GNSS-PWV prediction values. Compared with the actual measured GNSS-PWV values and RBF predicted PWV values, we find that the accuracy of GNSS-PWV predicted based on wavelet transform and RBF neural network is higher than that of the RBF neural network, and the accuracy of the prediction results decreases with the increase of the prediction time.