Title :
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
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;
Conference_Titel :
Innovations in Intelligent Systems and Applications (INISTA), 2012 International Symposium on
Conference_Location :
Trabzon
Print_ISBN :
978-1-4673-1446-6
DOI :
10.1109/INISTA.2012.6247025
