Conference Paper

Neuro-fuzzy models, BELRFS and LoLiMoT, for prediction of chaotic time series

DOI: 10.1109/INISTA.2012.6247025 Conference: Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on


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).

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Available from: Mahboobeh Parsapoor
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    • "The connectionist model is based on the neural structure of fear conditioning [11], which is a mechanism by which a biological system learns fearful stimuli to predict aversive events. The model is referred to as the Brain Emotional Learning based Fuzzy Recurrent System (BELRFS) and was introduced in [12]. "
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    • "Certainly, the emotional system's regions are very complex, and this structure has of course not mimicked all their connections in detail. The suggested structure has been the basis of Brain Emotional Learning-Inspired Models (BELIMs) [10], [12] such as the Brain Emotional Learning-based Fuzzy Inference System (BELFIS) [13],[14], the Brain Emotional Learning-based Recurrent Fuzzy System (BELRFS) [15],[16], and the Emotional Learning Inspired Ensemble Classifier (ELiEC) [17] "
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    ABSTRACT: This study presents comparative results obtained from employing four different neuro-fuzzy models to predict geomagnetic storms. Two of thes neuro-fuzzy models can be classified as Brain Emotional Learning Inspired Models (BELIMs) These two models are BELFIS (Brain Emotional Learning Based Fuzzy Inference System) and BELRFS (Brain Emotional Learning Recurrent Fuzzy System). The two other models are Adaptive Neuro-Fuzzy Inference System (ANFIS) and Locally Linear Model Tree (LoLiMoT) learning algorithm, two powerful neuro-fuzzy models to accurately predict a nonlinear system. These models are compared for their ability to predict geomagnetic storms using the AE index.
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