Adaptive brain emotional decayed learning for online prediction of geomagnetic activity indices

In this paper we propose adaptive brain-inspired emotional decayed learning to predict Kp, AE and Dst indices that characterize the chaotic activity of the earth's magnetosphere by their extreme lows and highs. In mammalian brain, the limbic system processes emotional stimulus and consists of two main components: Amygdala and Orbitofrontal Cortex (OFC). Here, we propose a learning algorithm for the neural basis computational model of Amygdala–OFC in a supervised manner and consider a decay rate in Amygdala learning rule. This added decay rate has in fact a neurobiological basis and yields to better learning and adaptive decision making as illustrated here. In the experimental studies, various comparisons are made between the proposed method named ADBEL, Multilayer Perceptron (MLP), Adaptive Neuro-Fuzzy Inference System (ANFIS) and Locally Linear Neuro-Fuzzy (LLNF). The main features of the presented predictor are the higher accuracy at all points especially at critical points, lower computational complexity and adaptive training. Hence, the presented model can be utilized in adaptive online prediction problems.