Identification and prediction of ionospheric dynamics using a Hammerstein-Wiener model with radial basis functions

To construct a model for ionospheric dynamics, a two step identification technique based on subspace algorithms is used. In the first step a Hammerstein model is identified using subspace algorithms and a basis function expansion for the input nonlinearities. In the second step the Wiener nonlinearity is identified as a standard least squares procedure. The inputs to the model are measurements made by the ACE satellite, which is located at the first Lagrangian point between the sun and the earth, while the outputs of the model are ground-based magnetometer readings. To avoid overfitting, the inputs are ranked in order of their effectiveness using an error search algorithm. Results for the ground-based magnetometer located at Thule in Greenland are presented.