基于深度学习的电离层参数预测研究

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

Abstract

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.

Key words: LSTM, ionosphere, multidimensional prediction, EMD

CLC Number:

TN92

Yuntian FENG, Xia WU, Xiong XU, Rongqing ZHANG. Research on ionospheric parameters prediction based on deep learning[J]. Journal on Communications, 2021, 42(4): 202-206.

Figures/Tables 6

References 10

[1]	周倜 . 海战场电磁态势生成若干关键技术研究[D]. 哈尔滨:哈尔滨工程大学, 2013.
	ZHOU T . Research on several key techniques of electromagnetic situation generation in sea battlefield[D]. Harbin:Harbin Engineering University, 2013.
[2]	高敬帆, 赵海生, 徐朝辉 ,等. 拉萨地区电离层长期变化特性研究[J]. 电波科学学报, 2018,33(6): 701-707.
	GAO J F , ZHAO H S , XU Z H ,et al. Long-term ionospheric characteristics over Lhasa[J]. Chinese Journal of Radio Science, 2018,33(6): 701-707.
[3]	张雯鹤, 黄国策, 董淑福 ,等. 基于LSTM的短波频率参数预测[J]. 空军工程大学学报(自然科学版), 2019,20(3): 59-64.
	ZHANG W H , HUANG G C , DONG S F ,et al. A prediction of frequency parameters based on LSTM for high frequency communication[J]. Journal of Air Force Engineering University (Natural Science Edition), 2019,20(3): 59-64.
[4]	孔庆颜, 柳文, 焦培南 ,等. 电离层 f₀F₂参数提前 24 小时预测[J]. 空间科学学报, 2009,29(4): 377-382.
	KONG Q Y , LIU W , JIAO P N ,et al. Twenty-four hour ahead prediction of f₀F₂[J]. Chinese Journal of Space Science, 2009,29(4): 377-382.
[5]	HAJIHASSANI M , JAHED A D , MARTO A ,et al. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm[J]. Bulletin of Engineering Geology and the Environment, 2015,74(3): 873-886.
[6]	HOCHREITER S , SCHMIDHUBER J . Long short-term memory[J]. Neural Computation, 1997,9(8): 1735-1780.
[7]	HUANG N E , SHEN Z , LONG S R ,et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis[J]. Proceedings of the Royal Society of London Series A:Mathematical,Physical and Engineering Sciences, 1998,454(1971): 903-995.
[8]	王昭斌, 胡伍生, 韩理想 ,等. 基于经验模态分解和BP神经网络的地铁沉降预测模型研究[J]. 现代测绘, 2017,40(5): 8-11.
	WANG Z B , HU W S , HAN L X ,et al. The study of the deformation of subway based on the empirical mode decomposition and back propagation neutral network[J]. Modern Surveying and Mapping, 2017,40(5): 8-11.
[9]	刘鑫 . 基于机器学习的短期电力负荷预测方法研究[D]. 北京:北京邮电大学, 2019.
	LIU X . Research on short-term load forecasting methods based on machine learning[D]. Beijing:Beijing University of Posts and Telecommunications, 2019.
[10]	DIEDERIK K , JIMMY B A . Adam:a method for stochastic optimization[J]. arXiv Preprint,arXiv:1412.6980, 2014.

Metrics

Recommended 0

No Suggested Reading articles found!

预测方法	f₀F₂	f_min	M(3 000)F₂	hpf
LSTM网络	0.77	0.148	0.03	4.35
多维预测	0.74	0.13	0.025	2.95
EMD算法	0.75	0.1	0.020 3	2.6

[1]	Hongyan WANG, Hai YUAN. Action recognition method based on fusion of skeleton and apparent features [J]. Journal on Communications, 2022, 43(1): 138-148.
[2]	Wengang MA, Yadong ZHANG, Jin GUO. Abnormal traffic detection method based on LSTM and improved residual neural network optimization [J]. Journal on Communications, 2021, 42(5): 23-40.
[3]	Guojun LI, Guangfa ZHENG, Changrong YE, Yinping ZHOU. Short-term prediction method of oblique sounding ionosphere MUF based on robust Kalman filter [J]. Journal on Communications, 2021, 42(1): 79-86.
[4]	Ruizhang HUANG, Wenfan JIN, Yanping CHEN, Yongbin QIN, Qinghua ZHENG. Research on Chinese predicate head recognition based on Highway-BiLSTM network [J]. Journal on Communications, 2021, 42(1): 100-107.
[5]	Han ZHANG,Yongjin HU,Yuanbo GUO,Jicheng CHEN. Research on coreference resolution technology of entity in information security [J]. Journal on Communications, 2020, 41(2): 165-175.
[6]	Limin MA,Jiahui WANG. Invertible secret image sharing scheme based on improved FEMD [J]. Journal on Communications, 2019, 40(7): 48-56.
[7]	WANG Li’na,GUO Xiaodong,WANG Run. Automated crowdturfing attack in Chinese user reviews [J]. Journal on Communications, 2019, 40(6): 1-13.
[8]	Run WANG,Benxiao TANG,Li’na WANG. DeepRD:LSTM-based Siamese network for Android repackaged applications detection [J]. Journal on Communications, 2018, 39(8): 69-82.
[9]	Shui-fei ZENG,Xiao-yan ZHANG,Xiao-feng DU,Tian-bo LU. New method of text representation model based on neural network [J]. Journal on Communications, 2017, 38(4): 86-98.
[10]	You-jun LI,Jia-jin HUANG,Hai-yuan WANG,Ning ZHONG. Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network [J]. Journal on Communications, 2017, 38(12): 109-120.
[11]	Wei-dong JIAO,Zhi-hu TANG,Xiao-yun SHEN. Analysis of influence of ionosphere and troposphere model on RAIM availability of COMPASS [J]. Journal on Communications, 2015, 36(3): 179-186.
[12]	. Combined wideband speech enhancement method based on statistical model and EMD [J]. Journal on Communications, 2013, 34(8): 13-101.
[13]	Xuan ZHOU,Chang-chun BAO,Bing-yin XIA. Combined wideband speech enhancement method based on statistical model and EMD [J]. Journal on Communications, 2013, 34(8): 95-101.
[14]	Wai-xi LIU,Shun-zheng YU,Ying GAO,Xiao HU. Opportunistic network coding based on prediction [J]. Journal on Communications, 2013, 34(4): 36-46.
[15]	. Opportunistic Network coding based on prediction [J]. Journal on Communications, 2013, 34(4): 5-46.

Research on ionospheric parameters prediction based on deep learning

RichHTML

PDF

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 6

References 10

Related Articles 15

Metrics

Recommended 0