Predicting Geomagnetic Activity Index by Brain Emotional Learning

The Emotional Learning Algorithm has been introduced to show the effect of emotions as well known stimuli in the quick and almost satisficing decision making in human. The remarkable properties of emotional learning, low computational complexity and fast training, and its simplicity in multi objective problems has made it a powerful methodology in real time control and decision systems, where the gradient based methods and evolutionary algorithms are hard to be used due to their high computational complexity. Recently the emotional approach has been successfully used to obtain multiple objectives in prediction problems of real world phenomena, more specifically space weather forecasting. A newer and more realistic model of emotional learning in human brain is used in this study to predict the most popular index of geomagnetic activity: Kp. The interplanetary Kp index is mainly used in warning and alert systems for satellites. The proposed model with brain emotional learning algorithm is introduced to make purposeful prediction of Kp index. Both the prediction accuracy during geomagnetic disturbances and substorms, and the rate of associated correct warning messages show the efficiency of this algorithm.