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Brain Emotional Learning Based Fuzzy Inference System (BELFIS) for Solar Activity Forecasting
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
2012 (English)In: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence (ICTAI 2012), Vol. 1, Piscataway, NJ: IEEE Press, 2012, p. 532-539, article id 6495090Conference paper, Published paper (Refereed)
Abstract [en]

This paper presents a new architecture based on a brain emotional learning model that can be us.ed in a wide varieties of AI applications such as prediction, identification and classification. The architecture is referred to as: Brain Emotional Learning Based Fuzzy Inference System (BELFIS) and it is developed from merging the idea of prior emotional models with fuzzy inference systems. The main aim of this model is presenting a desirable learning model for chaotic system prediction imitating the brain emotional network. In this research work, the model is used for predicting the solar activity, since it has been recognized as a threat to critical infrastructures in modern society. Specifically sunspot numbers are predicted by applying the proposed brain emotional learning model. The prediction results are compared with the outcomes of using other previous models like the locally linear model tree (LOLIMOT) and radial bias function (RBF) and adaptive neuro-fuzzy inference system (ANFIS). © 2012 IEEE.

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2012. p. 532-539, article id 6495090
Keywords [en]
brain emotional learning, fuzzy inference system, multi-year ahead prediction, solar activity forecasting, solar cycle 23, sunspot chaotic time series
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-19531DOI: 10.1109/ICTAI.2012.78ISI: 000320861900069Scopus ID: 2-s2.0-84876835024ISBN: 978-1-4799-0227-9 (print)ISBN: 978-0-7695-4915-6 (electronic)OAI: oai:DiVA.org:hh-19531DiVA, id: diva2:550806
Conference
24th IEEE International Conference on Tools with Artificial Intelligence, ICTAI 2012, Athens, Greece, November 7-9, 2012
Available from: 2012-09-07 Created: 2012-09-07 Last updated: 2018-03-22Bibliographically 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. p. v, 31
Series
Halmstad University Dissertations ; 8
National Category
Engineering and Technology
Identifiers
urn:nbn:se:hh:diva-25428 (URN)978-91-87045-16-5 (ISBN)978-91-87045-15-8 (ISBN)
Presentation
2014-06-17, 13:15 (English)
Opponent
Supervisors
Available from: 2014-06-02 Created: 2014-05-27 Last updated: 2018-05-24Bibliographically approved

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Parsapoor, MahboobehBilstrup, Urban

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