Chaotic time series prediction using brain emotional learning-based recurrent fuzzy system (BELRFS) Online publication date: Tue, 22-Oct-2013
by Mahboobeh Parsapoor; Urban Bilstrup
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 5, No. 2, 2013
Abstract: 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.
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