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Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model

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Abstract

The general regression neural network (GRNN) model was proposed to model and predict the length of day (LOD) change, which has very complicated time-varying characteristics. Meanwhile, considering that the axial atmospheric angular momentum (AAM) function is tightly correlated with the LOD changes, it was introduced into the GRNN prediction model to further improve the accuracy of prediction. Experiments with the observational data of LOD changes show that the prediction accuracy of the GRNN model is 6.1% higher than that of BP network, and after introducing AAM function, the improvement of prediction accuracy further increases to 14.7%. The results show that the GRNN with AAM function is an effective prediction method for LOD changes.

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Authors and Affiliations

  1. School of Geosciences and Info-Physics, Central South University, Changsha, 410083, China

    Qi-jie Wang  (王琪洁), Ya-nan Du  (杜亚男) & Jian Liu  (刘建)

Authors
  1. Qi-jie Wang  (王琪洁)
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  2. Ya-nan Du  (杜亚男)
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  3. Jian Liu  (刘建)
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Corresponding author

Correspondence to Qi-jie Wang  (王琪洁).

Additional information

Foundation item: Projects(U1231105, 10878026) supported by the National Natural Science Foundation of China

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Wang, Qj., Du, Yn. & Liu, J. Introducing atmospheric angular momentum into prediction of length of day change by generalized regression neural network model. J. Cent. South Univ. 21, 1396–1401 (2014). https://doi.org/10.1007/s11771-014-2077-2

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  • DOI: https://doi.org/10.1007/s11771-014-2077-2

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