Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS)
2013 (English)In: International Journal of Reasoning-based Intelligent Systems, ISSN 1755-0556, Vol. 5, no 2, 113-126 p.Article in journal (Refereed) Published
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 BELRFS, which stands for: Brain Emotional Learning-based Recurrent Fuzzy System. It adopts neuro-fuzzy adaptive networksto mimic the functionality of brain emotional learning. In particular, the model is investigated to predict space storms, since the phenomenon has been recognized 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. Copyright © 2013 Inderscience Enterprises Ltd.
Place, publisher, year, edition, pages
Olney, Bucks, UK: InderScience Publishers, 2013. Vol. 5, no 2, 113-126 p.
Brain emotional learning, Chaotic time series, Neuro-fuzzy adaptive networks, Linear Neuro-Fuzzy (LNF) with the Locally Linear Model Tree algorithm, Space weather forecasting, Solar activity forecasting
Electrical Engineering, Electronic Engineering, Information Engineering
IdentifiersURN: urn:nbn:se:hh:diva-24452DOI: 10.1504/IJRIS.2013.057273ScopusID: 2-s2.0-84892145721OAI: oai:DiVA.org:hh-24452DiVA: diva2:691068
Special Issue on Innovations of Intelligent Systems and Engineering; This paper is a revised and expanded version of a paper entitled ‘Neuro-fuzzy models, BELRFS and LoLiMoT, for prediction of chaotic time series’ presented at the INISTA’12, Trabzon, 2–4 July, 2012.2014-01-272014-01-272014-11-19Bibliographically approved