Abstract:Aiming at the low accuracy of the traditional tropospheric delay model, a high precision fusion model for the northern hemisphere is established based on the Hopfield model, using the neural network model error compensation technique. Taking the zenith tropospheric delay (ZTD) as the approximate “true value” of the meteorological sounding data of more than 120 observing stations in 2010 provided by the University of Wyoming, this paper analyzes and compares the Hopfield model, the traditional BP model, and the computational accuracy of the fusion model. The results show that the root mean square error (RMSE) of the Hopfield model is 35.31 mm, the RMSE of the traditional BP model is 30.34 mm, and the RMSE of the fusion model is 23.31 mm.
CHEN Yang,HU Wusheng,YAN Yuxiang et al. Research on Tropospheric Delay Model Based on Neural Network Model Error Compensation Technique[J]. jgg, 2018, 38(6): 577-580.