Abstract: Neural Network(NN) has been widely used in forecast of nonlinear systems over the past years. Generalized Regression Neural Network(GRNN), which is a new kind of NN, is proposed to predict satellite clock error because of its nonlinear characteristic. An slip window pattern is used organize sample data, which can raise utilization rate of data. In order to improve forecast ability of GRNN, the method called K-fold Cross-Validation is employed to train network. Furthermore, the optimal smoothing factor is determined in terms of Root Mean Square Error(RMSE). An experiment is carried out to verify GRNN effectiveness. Real satellite clock error data from International GNSS Service(IGS) is trained to construct the GRNN model, then this model is used to predict clock error. And also Clock error is forecasted using quadratic polynomial. Results show that GRNN model can reach ns-level prediction accuracy within 24 hour. Moreover, GNRR is more stable in comparison with quadratic polynomial. GRNN is worse than quadratic polynomial when predicting linear clock error, however, the former is obviously better than the latter when nonlinear clock error.

Key words: Generalized regression neural network, Quadratic polynomial, Clock error prediction, Cross validation, Slip window