Forecasting of ionospheric critical frequency using neural networks

Multilayer perception type neural networks (NN) are employed for forecasting ionospheric critical frequency (foF2) one hour in advance. The nonlinear black‐box modeling approach in system identification is used. The main contributions: 1. A flexible and easily accessible training database capable of handling extensive physical data is prepared, 2. Novel NN design and experimentation software is developed, 3. A training strategy is adopted in order to significantly enhance the generalization or extrapolation ability of NNs, 4. A method is developed for determining the relative significances (RS) of NN inputs in terms of mapping capability.