On the application of information theory to the optimum state-space reconstruction of the short-term solar radio flux (10.7 cm), and its prediction via a neural network

The prediction of a time series using a neural network involves an optimum state-space reconstruction. The state space of the daily 10.7-cm solar radio flux is reconstructed using an information theory approach. A multi-layer feed-forward neural net is used for short-term prediction of the time series. The convergence of the synaptic weights is obtained partially by simulated annealing and partially by the 'quick prop' variation of back-propagation. The result gives a reasonably accurate short-term prediction.