T-S fuzzy neural network to predict satellite clock bias

doi:10.11947/j.AGCS.2020.20190156

Abstract

Abstract: To find a high accuracy method for SCB(satellite clock bias) prediction based on characteristics of SCB data, this study proposes an T-S fuzzy neural network model based on the change rate method to predict SCB. the change rate of two SCB values of adjacent epoch is first calculated to obtain the corresponding change rate sequences. Then, modeling is performed based on the change rate sequence to predict the change rate value using the T-S fuzzy neural network model (TSFNN). Finally, the predicted sequences are recovered to the corresponding predicted SCB. The new model is compared with IGU-P products, quadratic polynomial (QP) model, and gray model (GM (1,1)) through the predicted sequences. The results show that the prediction precisions and stability of TSFNN model have been improved about 69.8% and 76.3% respectively after using the change rate method, and the accuracy is about 10 times higher than IGU-P products. the prediction effect of the proposed model is better than two common models. Therefore, the proposed model can achieve high accuracy prediction of SCB.

Key words: satellite clock bias, T-S fuzzy neural network, change rate, prediction

CLC Number:

P2287

WANG Xu, CHAI Hongzhou, WANG Chang. T-S fuzzy neural network to predict satellite clock bias[J]. Acta Geodaetica et Cartographica Sinica, 2020, 49(5): 580-588.

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