| MEEMD-Elman神经网络的电离层TEC预报模型 |
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| 引用本文： | 汤 俊,高 鑫.MEEMD-Elman神经网络的电离层TEC预报模型[J].大地测量与地球动力学,2020,40(4):395-399. |
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| 作者姓名： | 汤 俊 高 鑫 |
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| 作者单位： | 华东交通大学土木建筑学院;华东交通大学土木工程国家实验教学示范中心 |
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| 基金项目： | 国家自然科学基金(41761089);江西省自然科学基金(20181BAB203027);江西省教育厅科技项目(GJJ190345)。 |
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| 摘 要： | 利用IGS中心提供的不同纬度的电离层TEC值,建立基于改进的集总平均经验模态分解(MEEMD)算法和Elman回归神经网络(ERNN)模型相结合的电离层TEC预报模型。实验结果表明,在低、中、高不同纬度采用本文方法预报5d电离层TEC的预测值的均方根误差最优可达到0.96TECu,相对精度最优达到95.4%,精度较EMD-ERNN模型及单一ERNN模型有显著提高。
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| 关 键 词： | 电离层 总电子含量 MEEMD ELMAN 预报精度 |
Prediction Models of Ionospheric TEC by MEEMD and Elman Recurrent Neural Network |
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| TANG Jun,GAO Xin.Prediction Models of Ionospheric TEC by MEEMD and Elman Recurrent Neural Network[J].Journal of Geodesy and Geodynamics,2020,40(4):395-399. |
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| Authors: | TANG Jun GAO Xin |
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| Institution: | (School of Civil Engineering and Architecture,East China Jiaotong University,808 East-Shuanggang Street,Nanchang 330013,China;National Experimental Teaching Demonstration Center of Civil Engineering,East China Jiaotong University,808 East-Shuanggang Street,Nanchang 330013,China) |
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| Abstract: | In this paper, we combine modified ensemble empirical model decomposition (MEEMD) algorithm with Elman recurrent neural network (ERNN) to predict TEC by values of different latitudes provided by IGS center. At different latitudes which are under low, medium and high, the experimental results show that the smallest mean square errors of 5 days’ ionosphere TEC is 0.96 TECu and the best relative precision is 95.4%. Our model is better than the EMD-ERNN model and the single ERNN neural network model |
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| Keywords: | ionosphere TEC MEEMD Elman predicted accuracy |
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