(1. National Time Service Centre, Chinese Academy of Sciences, Xi′an 710600, China;
2. Key Laboratory of Time and Frequency Primary Standards, National Time Service Center,
Chinese Academy of Sciences, Xi′an 710600, China;
3. University of Chinese Academy of Sciences, Beijing 100039, China;
4. Key Laboratory of Precision Navigation and Timing Technology, National Time Service Center,
Chinese Academy of Sciences, Xi′an 710600, China)
Abstract: For avoiding the weakness of single model in predicting clock bias, a hybrid method combining the grey model(GM) and neural network(NN) for predicting clock bias is proposed. The basic idea, prediction model and practical process of clock bias predicting based on GM(1,1) and GRNN(generalized regression neural network) are presented. In view of the defects of traditional NN, the K-fold cross-validation algorithm is employed for improving the generalization ability of GRNN. For verifying the feasibility and validity of the hybrid method, the clock bias predictions are carried out by using the real data of GPS satellites clock bias and the prediction precisions for different methods are compared. The results show that the prediction precision for the proposed method is better than those for the GM(1,1) and the weighted combination of GM(1,1).