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, Oct 02, 2015
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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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    ABSTRACT: 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.
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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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