APPLICATION OF EXTREME LEARNING MACHINE IN SATELLITE
CLOCK ERROR PREDICTION
1) National Time Service Center, CAS, Xi’an 710600
Abstract Extreme Learning Machine (ELM) is employed for predicting satellite clock error. The impact of activation functions on prediction accuracy using ELM is analyzed, including Sigmoidal, Sine and Hardlim functions, and ELM model is compared with the grey system model and radial basis function (RBF) neural network(NN) model. The results show that prediction precision of ELM algorithm is best, and can learn faster than RBF neural network. Moreover, the Sigmoidal activation function is best for clock error prediction.
Key words ：
Extreme Learning Machine (ELM)
activation function
radial basis function neural network
satellite clock error
clock error prediction
Received: 27 February 2013
Published: 13 October 2013
Cite this article:
Lei Yu,Zhao Danning. APPLICATION OF EXTREME LEARNING MACHINE IN SATELLITE
CLOCK ERROR PREDICTION[J]. jgg, 2013, 33(5): 53-57.
Lei Yu,Zhao Danning. APPLICATION OF EXTREME LEARNING MACHINE IN SATELLITE
CLOCK ERROR PREDICTION[J]. jgg, 2013, 33(5): 53-57.
URL:
http://www.jgg09.com/EN/ OR http://www.jgg09.com/EN/Y2013/V33/I5/53
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