Journal of Northeastern University:Natural Science ›› 2017, Vol. 38 ›› Issue (5): 630-633.DOI: 10.12068/j.issn.1005-3026.2017.05.005

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Research on FAST Node Displacement Prediction Based on ELM Neural Network

SHA Yi1, CHEN Xi1, ZHANG Li-li1, ZHU Li-chun2   

  1. 1.School of Computer Science & Engineering, Northeastern University, Shenyang 110169, China; 2. National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100012, China.
  • Received:2015-12-14 Revised:2015-12-14 Online:2017-05-15 Published:2017-05-11
  • Contact: SHA Yi
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Abstract:

Due to the problems that the numbers of nodes in hidden layers of ELM neural network are in need of manual setting, and the over-fitting phenomenon is easy to appear, resulting in a reduction in the network generalization, an EM_ELM algorithm was proposed to improve ELM neural network based on error minimization. The feasibility was proved in theory which could improve the prediction accuracy and generalization of ELM neural network. Meanwhile, the algorithm was also applied into the model of FAST node displacement prediction and conducted simulation finally. The results show that although EM_ELM neural network is not sufficient in training time to a certain degree, it is still proper in real-time requirement. Besides, its prediction accuracy and generalization capabilities are enhanced, which is just a proof in the effectiveness and feasibility of the improved algorithm, thereby further illustrating that the EM_ELM neural network is more suitable for FAST node displacement prediction.

Key words: FAST node, ELM, neural network, displacement prediction, feasibility

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