OPTICS & OPTOELECTRONIC TECHNOLOGY, Volume. 23, Issue 1, 61(2025)

Short-Term Ionospheric TEC Prediction Based on Long Short Term Memory Network Model

MA Hui, LIAN Yu-qian, XU Na-na, JIANG Hao-nan, and DAI Huan-yao
Author Affiliations
  • Unit 63892 of the People's Liberation Army, Luoyang 471003, China
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    References(6)

    [2] [2] Klobuchar J A. Ionospheric Time-Delay Algorithm for Single-Frequency GPS Users[J]. IEEE Transactions on Aerospace & Electronic Systems, 1987, AES-23(3): 325-331.

    [3] [3] G A Mansilla. Evaluation of International Reference Ionosphere 2000 at a midlatitude station[J]. Radio Science, 2007, 42(2): 1-6.

    [4] [4] Nava B, Cosson P, Radicella S M. A new version of the NeQuick ionosphere electron density model[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2008, 70(15): 1856-1862.

    [7] [7] Habarulema J B, Mckinnell L A, Cilliers P J. Prediction of global positioning system total electron content using Neural Networks over South Africa[J]. Journal of Atmospheric and Solar-Terrestrial Physics, 2007, 69(15): 1842-1850.

    [9] [9] Huang Z, Yuan H. Ionospheric single-station TEC short-term forecast using RBF neural network[J]. Radio Science, 2014, 49(4): 283-292.

    [15] [15] S Sabour, N Frosst, GE Hinton. Dynamic routing between capsules[J]. Neural Information Processing Systems, 2017, DOI: 10.48550/arXiv.1710.09829.

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    MA Hui, LIAN Yu-qian, XU Na-na, JIANG Hao-nan, DAI Huan-yao. Short-Term Ionospheric TEC Prediction Based on Long Short Term Memory Network Model[J]. OPTICS & OPTOELECTRONIC TECHNOLOGY, 2025, 23(1): 61

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    Paper Information

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    Received: Sep. 8, 2024

    Accepted: Feb. 25, 2025

    Published Online: Feb. 25, 2025

    The Author Email:

    DOI:

    CSTR:32186.14.

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