| Abstract: | We investigated the dependence of the geomagnetic activity index K
p on the velocity and density of the solar wind and the intensity of the interplanetary magnetic field (IMF). A three-layer neural network was used to create the model. The degree of the influence of input parameters on K
p was determined by the value of the mean and root-mean square deviations of the model index values from the real indices. It was found that the largest contribution to the K
p index is provided by the Z component of the IMF, the velocity and density of the solar wind measured with a delay from 0 to 3 h relative to the studied value of K
p, and the previous value of the index itself. For the model with such a set of input parameters, the correlation coefficient between model and real series is ±0.89. The analysis of deviations from the real values of K
p showed that high indices are simulated worse than low indices. In order to solve this problem the data distribution was reduced to a uniform distribution over K
p, and this considerably decreased the standard deviations for large values of K
p. |
