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Prediction of Solar Cycle 24: Using a Connectionist Model of the Emotional System
Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES). School of Computer Science, Faculty of Engineering & Physic al Science, The University of Manchester, Manchester, United Kingdom.
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).ORCID iD: 0000-0001-6625-6533
2015 (English)In: 2015 International Joint Conference on Neural Networks (IJCNN), Piscataway, NJ: IEEE Press, 2015, 7280839Conference paper, (Other (popular science, discussion, etc.))
Abstract [en]

Accurate prediction of solar activity as one aspect of space weather phenomena is essential to decrease the damage from these activities on the ground based communication, power grids, etc. Recently, the connectionist models of the brain such as neural networks and neuro-fuzzy methods have been proposed to forecast space weather phenomena; however, they have not been able to predict solar activity accurately. That has been a motivation for the development of the connectionist model of the brain; this paper aims to apply a connectionist model of the brain to accurately forecasting solar activity, in particular, solar cycle 24. The neuro-fuzzy method has been referred to as the brain emotional learning-based recurrent fuzzy system (BELRFS). BELRFS is tested for prediction of solar cycle 24, and the obtained results are compared with well-known neuro-fuzzy methods and neural networks as well as with physical-based methods. @2015 IEEE

Place, publisher, year, edition, pages
Piscataway, NJ: IEEE Press, 2015. 7280839
Keyword [en]
brain emotional learning-based recurrent fuzzy system, emotional system, solar activity forecasting
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-29236DOI: 10.1109/IJCNN.2015.7280839ISI: 000370730603137Scopus ID: 2-s2.0-84951103535ISBN: 978-1-4799-1959-8 ISBN: 978-1-4799-1959-15 OAI: oai:DiVA.org:hh-29236DiVA: diva2:847120
Conference
2015 International Joint Conference on Neural Networks (IJCNN 2015), Killarney, Ireland, July 12–17, 2015
Available from: 2015-08-19 Created: 2015-08-19 Last updated: 2016-11-30Bibliographically approved

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Parsapoor, MahboobehBilstrup, UrbanSvensson, Bertil
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Citation style
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