Prediction of length-of-day variation using grey relational analysis and extreme learning machine

doi:10.7523/j.issn.2095-6134.2015.05.003

Abstract

Abstract:

Due to the time-varying characteristics of length-of-day (LOD), it is difficult to model LOD variations with a deterministic model. We employ a new type of artificial neural networks (ANN)-extreme learning machine (ELM) to predict LOD variations. In order to solve the problems of embedding dimension selection and network topology design, a training algorithm for ELM based on grey relational analysis (GRA) is first proposed. It optimizes the input and hidden layers simultaneously. Secondly, the values of LOD variation are preprocessed and a GRA-ELM model is then set up to accurately forecast LOD variation in near real-time. Finally, the prediction results are analyzed and compared with those obtained by the back propagation neural networks, generalization regression neural networks and Earth orientation parameters prediction comparison campaign. The results show that the prediction accuracy of our method is equal to or even better than those of the other prediction methods. The developed method is easy to use.

Key words: length-of-day (LOD) variations, prediction, grey relational analysis (GRA), extreme learning machine (ELM), neural networks (NN)

CLC Number:

P227.1

LEI Yu, CAI Hongbing, ZHAO Danning. Prediction of length-of-day variation using grey relational analysis and extreme learning machine[J]. Journal of University of Chinese Academy of Sciences, 2015, 32(5): 588-593.

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