Computer Science – Learning
Scientific paper
Jan 2011
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=2011acasn..52...54w&link_type=abstract
Acta Astronomica Sinica, vol. 52, no. 1, p. 54-61
Computer Science
Learning
Time, Methods: Data Analysis
Scientific paper
The atomic clock prediction plays an important role in time and frequency community. Because the conventional prediction models have advantages and limitations, the results of these models can be combined to synthesize the characteristics of various kinds of models. The combination model (CM) is constructed to predict the atomic clock. Actually, the results of CM are the weight averages of the results of single models. Considering the problem occurring in long clock time series when using CM, learning weight is put forward to modify this model. Therefore, the weights of single models are relative to learning weight in the modified CM. To demonstrate the efficiency of this proposed method, the clock data of 4 GPS satellites are chosen and CM is used to combine the results from the quadratic polynomial model and grey model. The result shows that the reliability is improved when CM is adopted. In comparison with CM, the modified CM can remarkably improve the stability and precision of the atomic clock prediction.
He Zai-Min
Hu Yong-Hui
Ma J.-H.
Wang Gou-Jen
Wang Kai
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