Research on ionospheric parameters prediction based on deep learning

被引：0

作者：

Feng Y. ^{[1
]}

Wu X. ^{[2
]}

Xu X. ^{[1
]}

Zhang R. ^{[3
]}

机构：

[1] State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang

[2] School of Electronics and Information Engineering, Tongji University, Shanghai

[3] School of Software Engineering, Tongji University, Shanghai

来源：

Tongxin Xuebao/Journal on Communications | 2021年 / 42卷 / 04期

关键词：

EMD; Ionosphere; LSTM; Multidimensional prediction;

D O I：

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

中图分类号：

学科分类号：

摘要：

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. © 2021, Editorial Board of Journal on Communications. All right reserved.

引用

页码：202 / 206

页数：4

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