摘要：In order to enhance the reproduction of the recovery phase D st index of a geomagnetic storm which has been shown by previous studies to be poorly reproduced when compared with the initial and main phases, an artificial neural network with one hidden layer and error back-propagation learning has been developed. Three hourly D st values before the minimum D st in the main phase in addition to solar wind data of IMF southward-component B s , the total strength B t and the square root of the dynamic pressure, (sqrt {n{V^2}}) , for the minimum D st , i.e., information on the main phase was used to train the network. Twenty carefully selected storms from 1972–1982 were used for the training, and the performance of the trained network was then tested with three storms of different D st strengths outside the training data set. Extremely good agreement between the measured D st and the modeled D st has been obtained for the recovery phase. The correlation coefficient between the predicted and observed D st is more than 0.95. The average relative variance is 0.1 or less, which means that more than 90% of the observed D st variance is predictable in our model. Our neural network model suggests that the minimum D st of a storm is significant in the storm recovery process.
