Title :
Neuro-fuzzy models for geomagnetic storms prediction: Using the auroral electrojet index
Author :
Parsapoor, Mahboobeh ; Bilstrup, Urban ; Svensson, Bertil
Author_Institution :
Sch. of Inf. Sci., Comput. & Electr. Eng. (IDE), Halmstad Univ., Halmstad, Sweden
Abstract :
This study presents comparative results obtained from employing four different neuro-fuzzy models to predict geomagnetic storms. Two of this 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.
Keywords :
fuzzy reasoning; geophysics computing; learning (artificial intelligence); magnetic storms; recurrent neural nets; AE index; ANFIS; BELFIS; BELIM; BELRFS; LoLiMoT; adaptive neuro-fuzzy inference system; auroral electrojet index; brain emotional learning based fuzzy inference system; brain emotional learning inspired models; brain emotional learning recurrent fuzzy system; geomagnetic storms prediction; locally linear model tree learning algorithm; neuro-fuzzy models; Adaptation models; Adaptive systems; Autoregressive processes; Brain models; Indexes; Mathematical model; Adaptive Neuro-fuzzy Inference System; Auroral Electrojet; Brain Emotional Learning-inspired Model; Locally linear model tree learning algorithm;
Conference_Titel :
Natural Computation (ICNC), 2014 10th International Conference on
Conference_Location :
Xiamen
Print_ISBN :
978-1-4799-5150-5
DOI :
10.1109/ICNC.2014.6975802
