Short-Term Prediction of BDS-3 Satellite Clock Errors Based on the GM-RBF Combined Model
In order to address the eigenproblem of the change of the trend term and random term of the satellite clock error,this paper proposes a method of the GM-RBF combined model.First,this model uses GM(1,1)to extract the pre-processed trend term of the satellite clock error,conducts modeling and forecasting to obtain the corresponding residual sequence,and uses the grey model to predict the obtained residual sequence by RBF neural network training.Then,the prediction result of the combined model can be obtained by adding the subsequent prediction value of the clock error of the GM(1,1)model and the residual prediction value of the RBF neural network.In order to verify the validity and feasibility of the combined model,the prediction results of the combined model are compared with those of the GM(1,1)model,the ARIMA model and the RBF neural network model.Experimental results show that the forecast accuracy of the combined model is higher than that of other single models,and that its average forecast accuracy can be increased by 46.4%～86.2%in different periods.
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