【摘要】 针对短波最大可用频率（MUF）经典模型方法预测精度低及机器学习方法训练集数据获取难度大的问题，提出一种模型数据双驱动的双向门控递归单元（Bi GRU）网络用于MUF短期预测。模型驱动方面，利用经典MUF预测模型生成的大规模数据集作为模型驱动训练集，经过2DCNN和Bi GRU网络联合学习后，获得一个初步网络。数据驱动方面，使用小规模的实测数据集对初步网络进行二次训练，得到最终网络CNN-Bi GRU-NN。仿真结果表明，所提网络与GRU网络、LSTM网络以及VOACAP模型相比，在日期尺度和时刻尺度上的平均均方根误差（RMSE）均有降低。更多还原

【Abstract】 Predicting the maximum available frequency of short-wave communication presents the challenges of low prediction accuracy of classical prediction model methods and difficulty in obtaining training set data for machine learning prediction methods. To address this issue, a model-data dual-driven bidirectional gated recurrent unit(BiGRU) network for short-term prediction of MUF was proposed. On the model-driven, a large-scale dataset generated by the classical MUF prediction model was used as the model-driven training set, and a preliminary network was obtained after joint learning of the 2D CNN and the BiGRU network. On the data-driven, the preliminary network was trained twice using a small-scale measured dataset to obtain the final network CNN-BiGRU-NN. The simulation results show that the proposed network has reduced average root mean squared error(RMSE) at both daily and momentary scales compared with the GRU network, LSTM network and VOACAP model.更多还原