Publications:Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS)
From CERES
| Title | Chaotic Time Series Prediction Using Brain Emotional Learning Based Recurrent Fuzzy System (BELRFS) |
|---|---|
| Author | Mahboobeh Parsapoor and Urban Bilstrup |
| Year | 2013 |
| PublicationType | Journal Paper |
| Journal | International Journal of Reasoning-based Intelligent Systems |
| HostPublication | |
| DOI | http://dx.doi.org/10.1504/IJRIS.2013.057273 |
| Conference | |
| Diva url | http://hh.diva-portal.org/smash/record.jsf?searchId=1&pid=diva2:691068 |
| Abstract | 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. |
