Research on ionospheric parameters prediction based on deep learning

Yuntian FENG; Xia WU; Xiong XU; Rongqing ZHANG

doi:10.11959/j.issn.1000-436x.2021097

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Research on ionospheric parameters prediction based on deep learning

Correspondences | 更新时间：2024-06-05

- Research on ionospheric parameters prediction based on deep learning
- Journal on Communications Vol. 42, Issue 4, Pages: 202-206(2021)
- 作者机构：
  
  1. 电子信息系统复杂电磁环境效应国家重点实验室，河南洛阳 471003
  2. 同济大学电子与信息工程学院，上海 201804
  3. 同济大学软件学院，上海 201804
- 作者简介：
- 基金信息：
  
  The Open Research Fund of State Key Laboratory CEMEE(CEMEE2020K0104B);The National Key Re-search and Development Program of China(2017YFE0119300)
- DOI：10.11959/j.issn.1000-436x.2021097
  CLC： TN92
- Online First：2021-04，
  
  Published：25 April 2021
- 稿件说明：
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Yuntian FENG, Xia WU, Xiong XU, et al. Research on ionospheric parameters prediction based on deep learning[J]. Journal on Communications, 2021, 42(4): 202-206.
DOI：

Yuntian FENG, Xia WU, Xiong XU, et al. Research on ionospheric parameters prediction based on deep learning[J]. Journal on Communications, 2021, 42(4): 202-206. DOI： 10.11959/j.issn.1000-436x.2021097.

摘要

对于电离层参数预测，通过长短期记忆（LSTM）的预测神经网络建模实现电离层参数的短期和日均值预测。使用逐点预测和序列预测2种方法，并采用多维预测和经验模态分解（EMD）算法优化，预测电离层参数的每小时和每天的变化规律。实验结果验证了所提优化算法在提高预测电离层参数预测精度上的可行性。

Abstract

For ionospheric parameter prediction

the short-term and daily mean value prediction of ionospheric parameters was established by long short-term memory (LSTM) predictive neural network modeling.Two methods of point-by-point prediction and sequence prediction were utilized.Furthermore

in order to predict the hourly and daily changes of ionospheric parameters

the proposed scheme was optimized by multidimensional prediction and empirical mode decomposition (EMD) algorithm.Finally

the feasibility of the proposed optimization algorithm in improving the prediction accuracy of ionospheric parameters is verified.

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references

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