应用BP神经网络针对高通量质子事件的通量数值预报

Application of BP Neural Network in Prediction of Proton Events Peak Flux

质子事件的爆发与太阳软X射线辐射有着很强的相关性,利用GOES卫星的1～8 (?)波段和0．5～4 (?)波段的软X射线数据,选取一些特征参量验证该相关性并应用到质子事件短期预报中．在当前质子事件传输物理机制不完全明确的情况下,在现有的预报质子事件有无的模型基础上,利用BP神经网络,根据软X射线通量水平等预测事件质子峰值通量水平,再对训练后的网络进行检验,检验预测所得结果与实际探测值误差小于一个量级,具备一定实用意义．

Solar proton events especially those with high fluxes may cause threat to the spacecrafts and satellites round the orbits near the earth, and may cause damage to the sensitive electronic components on the satellites, therefore, accurate short-term prediction of proton events is very meaningful to assure the safety of the space task and coordinate the instruments aboard the satellites. The current research shows that there exist a considerable correlation between proton events and soft X-ray radiation, so in this paper, based on the 1 ～ 8 A and 0.5 ～ 4A band soft X-ray data from GOES database, and choosing some characteristic parameters for our proton prediction model, a BP neural network was designed and used to predict the peak flux of the proton events, with the network input of soft X-ray data. The test result shows that in most cases the prediction error is less than one order. Key words Soft X-ray, Proton events, BP neural network, Peak flux prediction