计算机工程与应用2017，Vol.53Issue(24)：252-256,262,6.DOI:10.3778/j.issn.1002-8331.1606-0395
EEMD与RBF神经网络的太阳黑子月均值预测
EEMD and RBF neural network prediction of sunspot monthly mean
摘要
Abstract
The sunspot monthly mean is a typical chaotic time series. It has strong nonlinear and non-stationary character-istics, and can reflect the true level of the solar activity. A forecasting model of combinating Ensemble Empirical Mode Decomposition(EEMD)with Radial Basis Function(RBF)neural network is adopted. The original time series is decom-posed into a number of different time scales intrinsic mode function by using EEMD, and then these components are mod-eled and predicted. The predicted value of the original time series is reconstructed by the predictive value of each compo-nent. The model not only reduces the complexity of the algorithm, but also improves the physical meaning of the modal components. The simulation results show that compared with the Empirical Modal Decomposition(EMD)and RBF com-bination model, the model has higher prediction accuracy.关键词
太阳黑子/集合经验模态分解/径向基函数(RBF)神经网络/预测Key words
sunspot/ensemble empirical mode decomposition/Radial Basis Function(RBF)neural network/prediction分类
信息技术与安全科学引用本文复制引用
孙堂乐,李国辉..EEMD与RBF神经网络的太阳黑子月均值预测[J].计算机工程与应用,2017,53(24):252-256,262,6.基金项目
陕西省自然科学基金(No.2014JM8331). （No.2014JM8331）
