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

Authors: Mahboobeh Parsapoor; Urban Bilstrup

Addresses: School of Information Science, Computer and Electrical Engineering (IDE), Halmstad University, Halmstad, Sweden ' School of Information Science, Computer and Electrical Engineering (IDE), Halmstad University, Halmstad, Sweden

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.

Keywords: auroral electrojet index; brain emotional learning; chaotic time series; chaos theory; recurrent neuro-fuzzy adaptive networks; LNF; linear neuro-fuzzy; locally linear model tree; reasoning-based intelligent systems; space storms; space weather forecasting; solar activity forecasting; sunspot number time series; neural networks; fuzzy logic; prediction models; modelling; critical infrastructures; prediction accuracy.

DOI: 10.1504/IJRIS.2013.057273

International Journal of Reasoning-based Intelligent Systems, 2013 Vol.5 No.2, pp.113 - 126

Received: 08 May 2021
Accepted: 12 May 2021

Published online: 20 Oct 2013 *

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