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

Author

Parsapoor, Mahboobeh ; Bilstrup, Urban

Author_Institution

Sch. of Inf. Sci., Halmstad Univ., Halmstad, Sweden

fYear

2012

fDate

2-4 July 2012

Firstpage

1

Lastpage

5

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).

Keywords

fuzzy neural nets; learning (artificial intelligence); time series; BELRFS; Ikeda time series prediction; LOLIMOT; Lorenz time series prediction; brain emotional learning-based recurrent fuzzy system; chaotic time series prediction; local linear neuro-fuzzy models with linear model tree algorithm; mammal brain-inspired learning system; Biological system modeling; Brain models; Computational modeling; Neurons; Predictive models; Time series analysis; LoLiMoT; brain emotional learning; neuro-fuzzy mode; prediction chaotic time series;

fLanguage

English

Publisher

ieee

Conference_Titel

Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on

Conference_Location

Trabzon

Print_ISBN

978-1-4673-1446-6

Type

conf

DOI

10.1109/INISTA.2012.6247025

Filename

6247025