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Forecasting Solar Activity with Computational Intelligence Models
McGill University, Montreal, QC H3A 2T6, Canada.
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
2018 (English)In: IEEE Access, E-ISSN 2169-3536, Vol. 6, p. 70902-70909Article in journal (Refereed) Published
Abstract [en]

It is vital to accurately predict solar activity, in order to decrease the plausible damage of electronic equipment in the event of a large high-intensity solar eruption. Recently, we have proposed brain emotional learning-based fuzzy inference system (BELFIS) as a tool for the forecasting of chaotic systems. The structure of BELFIS is designed based on the neural structure of fear conditioning. The function of BELFIS is implemented by assigning adaptive networks to the components of the BELFIS structure. This paper especially focuses on the performance evaluation of BELFIS as a predictor by forecasting solar cycles 16-24. The performance of BELFIS is compared with other computational models used for this purpose, in particular with the adaptive neuro-fuzzy inference system. © 2018 IEEE.

Place, publisher, year, edition, pages
Piscataway, N.J.: Institute of Electrical and Electronics Engineers Inc. , 2018. Vol. 6, p. 70902-70909
Keywords [en]
Adaptive systems, Brain, Brain models, Chaotic systems, Forecasting, Fuzzy logic, Fuzzy neural networks, Fuzzy systems, Intelligent control, Neural networks, Oscillators (electronic), Solar energy, Solar radiation, Time series analysis, Adaptive neuro-fuzzy inference system, Brain emotional learning, Predictive models, Solar activity, Solar cycle, Fuzzy inference
National Category
Computer Systems Computer Sciences Bioinformatics (Computational Biology)
Identifiers
URN: urn:nbn:se:hh:diva-38724DOI: 10.1109/ACCESS.2018.2867516ISI: 000453302000001Scopus ID: 2-s2.0-85056589499OAI: oai:DiVA.org:hh-38724DiVA, id: diva2:1276357
Available from: 2019-01-08 Created: 2019-01-08 Last updated: 2019-01-08Bibliographically approved

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Bilstrup, UrbanSvensson, Bertil

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