Chaotic time series prediction using brain emotional learning-based recurrent fuzzy system (BELRFS)
Mahboobeh Parsapoor, Urban Bilstrup · International Journal of Reasoning-based Intelligent Systems · 2013
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.