A wavelet neural network for optimal wavelet function to predict GPS satellite clock bias

doi:10.11947/j.AGCS.2020.20190180

Abstract

Abstract: To develop the accuracy for predicting SCB based on the the problem that the wavelet neural network (WNN) model fails to select the appropriate wavelet function according to the actual situation, an wavelet neural network for Optimal Wavelet Function based on Shannon entropy-energy ratio to predict SCB is proposed herein. The wavelet coefficients are obtained by carring on the continuous wavelet decomposition to the clock a once difference sequences. Then, the energy value and Shannon's entropy value of the wavelet coefficient are calculated respectively, and the “Shannon's entropy-energy ratio” (SEE) is taken as the evaluation index for the selection of the optimal wavelet function to induct select the most suitable wavelet function as the activation function of WNN model. Finally, the optimal WNN model is used to predict SCB, and the predicted results are compared and analyzed. The results show that the evaluation index can accurately guide WNN model to choose the appropriate wavelet function according to the actual situation of SCB, improve the prediction accuracy and applicability of WNN model, and enable the model to realize high accuracy SCB prediction.

Key words: satellite clock bias(SCB), energy, Shannon's entropy, prediction, wavelet neural network

CLC Number:

P228

WANG Xu, CHAI Hongzhou, WANG Chang, CHONG Yang. A wavelet neural network for optimal wavelet function to predict GPS satellite clock bias[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(8): 983-992.

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