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Neuro-Fuzzy Models, BELRFS and LOLIMOT, for Prediction of Chaotic Time Series
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: INISTA 2012: International Symposium on Innovations in Intelligent Systems and Applications : 2-4 July, 2012 : Trabzon, Turkey, Piscataway, N.J.: IEEE Press, 2012, Article number 6247025- p., 624702Conference paper, Published paper (Refereed)
Abstract [en]

This paper suggests a novel learning model for prediction of chaotic time series, brain emotional learning-based recurrent fuzzy system (BELRFS). The prediction model is inspired by the emotional learning system of the mammal brain. BELRFS is applied for predicting Lorenz and Ikeda time series and the results are compared with the results from a prediction model based on local linear neuro-fuzzy models with linear model tree algorithm (LoLiMoT). © 2012 IEEE.

Place, publisher, year, edition, pages
Piscataway, N.J.: IEEE Press, 2012. Article number 6247025- p., 624702
Keyword [en]
brain emotional learning, LoLiMoT, neuro-fuzzy mode, prediction chaotic time series
National Category
Computer Systems
Identifiers
URN: urn:nbn:se:hh:diva-19529DOI: 10.1109/INISTA.2012.6247025Scopus ID: 2-s2.0-84866603396ISBN: 978-1-4673-1446-6 (print)ISBN: 978-1-4673-1448-0 (electronic)OAI: oai:DiVA.org:hh-19529DiVA: diva2:550803
Conference
International Symposium on Innovations in Intelligent Systems and Applications (INISTA 2012), Trabzon, Turkey, 2-4 July, 2012
Available from: 2012-09-07 Created: 2012-09-07 Last updated: 2017-04-27Bibliographically approved

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
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More languages
Output format
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  • asciidoc
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