Chaotic time series prediction using brain emotional learning-based recurrent fuzzy system (BELRFS)

In this paper, an architecture based on the anatomical structure of the emotional network in the brain of mammalians is applied as a prediction model for chaotic time series studies. The architecture is called Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS), which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networks to mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognised as a threat to critical infrastructure in modern society. To evaluate the performance of BELRFS, three benchmark time series: Lorenz time series, sunspot number time series and Auroral Electrojet (AE) index. The obtained results of BELRFS are compared with Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm (LoLiMoT). The results indicate that the suggested model outperforms most of data driven models in terms of prediction accuracy.