大气科学学报2012，Vol.35Issue(4)：508-512,5.
基于神经网络的太阳黑子面积平滑月均值预测
Prediction of the smoothed monthly mean sunspot area based on neural network
摘要
Abstract
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.关键词
太阳活动/预测/太阳黑子面积/BP神经网络Key words
solar active/prediction/solar sunspot area/artificial neural network分类
天文与地球科学引用本文复制引用
丁留贯,蓝如师,蒋勇,彭建东..基于神经网络的太阳黑子面积平滑月均值预测[J].大气科学学报,2012,35(4):508-512,5.基金项目
国家自然科学基金资助项目 （）
江苏省研究生科研创新基金项目（CXZZ11_0625 （）
CXZZ12_0510）.太阳黑子月均值数据由http：//solarscience.msfc.nasa.gov/提供.谨致谢忱! （）
