CAJ | 학술논문

采用第23太阳活动周 X 级以上耀斑的数据，通过回归分析、Gauss 拟合和 RBF 人工神经网络等方法对 X 级以上耀斑进行预报研究.结果表明，将黑子群的位置、卡灵顿经度、耀斑爆发时间与黑子群达到最大面积的时间关系、每7 d 黑子群的最大面积、太阳耀斑流量的积分值、CME 速度和 F10.7射电流量7个预报因子作为参量对 RBF 人工神经网络预报模型进行训练，训练后建立的 RBF 模型的输出结果和训练数据的相关系数高达98%，对耀斑强度的预报结果与观测结果的误差在0.5以内，预报模型符合耀斑短期预报的要求.
채용제23태양활동주 X 급이상요반적수거，통과회귀분석、Gauss 의합화 RBF 인공신경망락등방법대 X 급이상요반진행예보연구.결과표명，장흑자군적위치、잡령돈경도、요반폭발시간여흑자군체도최대면적적시간관계、매7 d 흑자군적최대면적、태양요반류량적적분치、CME 속도화 F10.7사전류량7개예보인자작위삼량대 RBF 인공신경망락예보모형진행훈련，훈련후건립적 RBF 모형적수출결과화훈련수거적상관계수고체98%，대요반강도적예보결과여관측결과적오차재0.5이내，예보모형부합요반단기예보적요구.
This paper presents a statistical study on the flares based on the data of X-class and above flares in 23 solar cycles.The methods of regression analysis,Gauss fitting and RBF artificial neural networks are used to study a series of predictors.The statistical results show the correlation coefficient between output results of RBF model,which is established to be trained,and training data can reach 98%,while the error between the predicted results and the observations results is less than 0.5.The RBF artificial neural networks model contains seven parameters, including sunspot group of position, absolute longitude,the most widespread spots in each 7 days,the relationship between the start time of flares and the time of sunspot groups that achieve the most widespread,integral values of solar flares,F10.7 radio flux and the speed of CMEs.These seven parameters are used to train the RBF model.Based on this model,the flares can be forecasted for a short period.