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which is actually a neural network autoregressive model with additional input signals (NNARX). Our analysis
is focused on choice of X parts of NNARX model in order to capture middle and long term dependencies. Daily
distribution of prediction error suggests need for structural changes of the neural network model, as well as
adaptation of running average lengths used for determination of X inputs. Generalisation properties of proposed
neural predictor are improved by carefully designed pruning procedure with additional regularisation term in
criterion function. Some results from the NNARX model are presented to illustrate the feasibility of using such
a model as ionospheric storm forecasting technique.
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