Abstract:We focus on the problems of low accuracy and easy to fall into local optimum in the short-term prediction of ionospheric TEC based on neural network. We use the TEC data and geomagnetic activity index provided by the CODE center to establish an improved Elman neural network model based on the sparrow search algorithm(SSA). The BP model, Elman model and SSA-Elman combined model are used to predict 5 days continuous TEC in the middle and low latitudes during the ionospheric quiet period and disturbance period. The experimental results show that when the optimized Elman neural network model is used to predict 5 days continuous TEC, the root mean square error of single day can reach 1.443 TECu, and the correlation coefficient can reach 0.976, which is better than BP model and Elman neural network model.
TANG Jun,ZHONG Zhengyu,LI Yinjian et al. Short-Term Prediction Model of Ionospheric TEC Based on SSA-Elman Neural Network[J]. jgg, 2022, 42(4): 378-383.