Prediction of the Smoothed Monthly Mean Sunspot Numbers by Means of RBF (Radial Basic Function) Neural Networks

The Radial Basis Function (RBF) neural networks method is introduced and applied to the mid‐term solar prediction. The smoothed monthly mean sunspot number (SMMSN) are predicted by the method using solar cycle 23 data in this paper. Predictions of SMMSNs are made respectively for the next eight months by training the neural networks with different sets of data. Comparisons of these with the derived ones from the observational data for the most part of cycle 23 show that this RBF neural networks method should be an applicable one for the mid‐term solar activity forecast. A brief discussion given in the last section of this paper points out: (1) that the error of the prediction increases along with the time in advance, while for the prediction with an advanced time of≤4 months the error can be controlled below 4.8 and 38%, and for 89% of this kind of prediction the relative error is no more than 15%. (2) that the size of the data set used for the training of the RBF neural networks would given an effect on the predicting ability of the prediction model.