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

ABSTRACT 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).

Full-text

Available from: Mahboobeh Parsapoor, Jun 02, 2015
0 Followers
 · 
342 Views
  • Source
    [Show abstract] [Hide abstract]
    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.
    The 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'13); 08/2014
  • Source
    9th International Symposium Advances in Artificial Intelligence and Applications (AAIA'14), Warsaw, Poland; 09/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In this thesis the mammalian nervous system and mammalian brain have been used as inspiration to develop a computational intelligence model based on the neural structure of fear conditioning and to extend the structure of the previous proposed amygdala-orbitofrontal model. The proposed model can be seen as a framework for developing general computational intelligence based on the emotional system instead of traditional models on the rational system of the human brain. The suggested model can be considered a new data driven model and is referred to as the brain emotional learning-inspired model (BELIM). Structurally, a BELIM consists of four main parts to mimic those parts of the brain’s emotional system that are responsible for activating the fear response. In this thesis the model is initially investigated for prediction and classification. The performance has been evaluated using various benchmark data sets from prediction applications, e.g. sunspot numbers from solar activity prediction, auroral electroject (AE) index from geomagnetic storms prediction and Henon map, Lorenz time series. In most of these cases, the model was tested for both long-term and short-term prediction. The performance of BELIM has also been evaluated for classification, by classifying binary and multiclass benchmark data sets.
    06/2014