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

fYear

2014

fDate

19-21 Aug. 2014

Firstpage

12

Lastpage

17

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2014 10th International Conference on

Conference_Location

Xiamen

Print_ISBN

978-1-4799-5150-5

Type

conf

DOI

10.1109/ICNC.2014.6975802

Filename

6975802