%0 Journal Article
%A SUN Tangle
%A LI Guohui
%T EEMD and RBF neural network prediction of sunspot monthly mean
%D 2017
%R 10.3778/j.issn.1002-8331.1606-0395
%J Computer Engineering and Applications
%P 252-256
%V 53
%N 24
%X The sunspot monthly mean is a typical chaotic time series. It has strong nonlinear and non-stationary characteristics, 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 decomposed into a number of different time scales intrinsic mode function by using EEMD, and then these components are modeled and predicted. The predicted value of the original time series is reconstructed by the predictive value of each component. 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 combination model, the model has higher prediction accuracy.
%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1606-0395