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Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS)
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
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
Abstract [en]

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.
Keyword [en]
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
National Category
Computer Science
Identifiers
URN: urn:nbn:se:hh:diva-24452DOI: 10.1504/IJRIS.2013.057273Scopus ID: 2-s2.0-84892145721OAI: oai:DiVA.org:hh-24452DiVA: diva2:691068
Funder
Knowledge Foundation
Note

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.

Available from: 2014-01-27 Created: 2014-01-27 Last updated: 2017-04-03Bibliographically approved
In thesis
1. Brain Emotional Learning-Inspired Models
Open this publication in new window or tab >>Brain Emotional Learning-Inspired Models
2014 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.

Place, publisher, year, edition, pages
Halmstad: Halmstad University Press, 2014
Series
Halmstad University Dissertations, 8
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-25428 (URN)
Presentation
2014-06-17, 13:15 (English)
Opponent
Supervisors
Available from: 2014-06-02 Created: 2014-05-27 Last updated: 2014-08-21Bibliographically approved

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