For mid-term forecast of the daily index of solar 10.7 cm radio flux with deep learning method,a neural network based on classical multi-layer perception model is proposed.The network contains only one hidden layer with 90 neutrons,and an autoregressive model of time series is implemented non-parametrically.In the forecast,historical daily indices as well as historical forecast error are considered.The model gives forecast of next 27 days with values of past 27 days.The network is trained and validated with historical data over 50 years,and the result clearly shows that the mean relative error is significantly reduced compared to the traditional methods.Unlike most of previous studies,in which the parameters of the model need to be rolling-updated,the parameters are fixed after the training with this model.The proposed model greatly simplifies daily operation of forecast and is extremely advantageous to the promotion in other applications.
solar activityF107 fluxforecastdeep learningneural network
NETL
NSTL
万方数据
