基于神经网络的太阳黑子面积平滑月均值预测

黑子面积数是表征太阳活动的重要物理量，准确预测黑子面积能为太阳活动研究、空间天气业务等提供重要参考依据。本文提出一种基于BP神经网络的黑子面积平滑月均值预测方法，利用第20个太阳周之前的数据对网络进行训练，建立预测模型。对第21个太阳周至今的数据进行预测试验，并考虑不同训练步长、预测步长对模型精度的影响。结果表明，该模型能准确逐月预测黑子面积，采用不同训练步长时相对误差均不超过5％，进行更长时间的预测，相对误差会逐渐增大。

Prediction of the smoothed monthly mean sunspot area based on neural network

Sunspot area is an important feature to measure the solar activities. Prediction of sunspot area can provide useful information for solar activities and space weather studies, etc. In this paper, we pro-pose a smoothed monthly mean sunspot area prediction method by using an artificial neural network. The prediction model is built by training the area data before the twentieth solar cycle, and then it is used to forecast the data after the twenty-first solar cycle. We also consider the influence of different training steps and prediction steps respectively. The proposed method is able to exactly forecast the sun-spot area of the next month, and the relative errors for different training steps are all less than 5 %. However, the relative error will get larger if the prediction time is longer.