全球定位系统2025，Vol.50Issue(1)：48-59,12.DOI:10.12265/j.gnss.2024095
基于ICEEMDAN和SSA-LSTM组合模型的电离层TEC预测
Ionospheric TEC prediction based on ICEEMDAN and SSA-LSTM combined models
摘要
Abstract
Aiming at the nonlinear and non-stationary characteristics of ionospheric total electron content(TEC)and the problems that a single LSTM model has in prediction,such as low accuracy and easy to fall into local optimality,On the basis of improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)and sample entropy(SE)algorithms,combined with sparrow search algorithm(SSA)and long short-term memory(LSTM)neural network,a combined prediction model for TEC in the ionosphere was constructed,and analyzes the prediction accuracy of ionospheric TEC during the low year calm period of solar activity and the high year disturbance period of solar activity for 5 consecutive days.The experimental results show that compared with the single LSTM model and the SSA-LSTM model,the root mean square error of the combined model in this paper is reduced by 1.06 TECU and 2.25 TECU respectively under different latitude and longitude of the low solar activity quiet period and the high solar activity disturbance period.The average absolute error decreased by 0.74 TECU and 1.68 TECU respectively,and the average relative accuracy increased by 7.63%and 8.97%respectively.The prediction effect of the combined model was significantly better than that of the single LSTM model and the SSA-LSTM model.关键词
电离层/总电子含量(TEC)预测/改进的自适应噪声完备集合经验模态分解(ICEEMDAN)/样本熵(SE)/麻雀搜索算法(SSA)/长短期记忆神经网络(LSTM)Key words
ionosphere/prediction of total electron content(TEC)/improved complete ensemble empirical mode decomposition with adaptive noise(ICEEMDAN)/sample entropy(SE)/sparrow search algorithm(SSA)/long short-term memory neural network(LSTM)分类
测绘与仪器引用本文复制引用
张振国,孙希延,纪元法,贾茜子..基于ICEEMDAN和SSA-LSTM组合模型的电离层TEC预测[J].全球定位系统,2025,50(1):48-59,12.基金项目
广西科技厅项目(桂科AA23062038,桂科AD22080061,桂科AB23026120,桂科ZY22096026) （桂科AA23062038,桂科AD22080061,桂科AB23026120,桂科ZY22096026）
国家自然科学基金(U23A20280,62161007,62061010) （U23A20280,62161007,62061010）
"认知无线电与信息处理"教育部重点实验室2023年主任基金项目(CRKL230104) （CRKL230104）
广西高校中青年教师科研基础能力提升项目(2022KY0181) （2022KY0181）
广西研究生教育创新计划项目(YCSW2024329) （YCSW2024329）
