High precision ionospheric Total Electron Content(TEC) prediction is of great significance for improving the accuracy of Global Navigation Satellite System(GNSS), preventing natural disasters, and ensuring wireless communication. Given the varying frequencies of TEC signals, a hybrid CEEMDAN-BiLSTM-PSO-LSSVM-FE model for predicting ionospheric TEC content is proposed in this paper. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the extracted hourly TEC sequence and calculate the fuzzy entropy (FE) of the subsequences. Then, the signal is divided into high-frequency and low-frequency parts based on the fuzzy entropy value, for the high-frequency component, Bidirectional Long Short-Term Memory network (BiLSTM) is used for prediction; for the low-frequency component, Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) is used for prediction. The hourly TEC values collected from six evenly distributed GPS stations in China are used as the main input variable for the proposed model, with solar and geomagnetic activity data used as auxiliary data, using the TEC data from the previous 48 h to forecast the TEC content for the next hour.

中文翻译：

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使用 BiLSTM 和 PSO-LSSVM 进行 TEC 预测的混合模型

高精度电离层总电子含量（TEC）预测对于提高全球导航卫星系统（GNSS）精度、预防自然灾害、保障无线通信具有重要意义。考虑到TEC信号的频率变化，本文提出了一种用于预测电离层TEC含量的混合CEEMDAN-BiLSTM-PSO-LSSVM-FE模型。首先，采用自适应噪声完全集合经验模态分解（CEEMDAN）对提取的每小时TEC序列进行分解，并计算子序列的模糊熵（FE）。然后，根据模糊熵值将信号分为高频部分和低频部分，对于高频部分，采用双向长短期记忆网络（BiLSTM）进行预测；对于低频分量，采用基于粒子群优化的最小二乘支持向量机（PSO-LSSVM）进行预测。该模型以国内6个均匀分布的GPS站采集的每小时TEC值作为主要输入变量，以太阳和地磁活动数据为辅助数据，利用前48 h的TEC数据来预测TEC含量接下来的一个小时。