Forecasting foreign exchange rates using an IT2 FCM based IT2 neuro-fuzzy system

Author

Fallahzadeh, Emad ; Montazeri, Mohammad Ali

Author_Institution

Dept. of Electr. & Comput. Eng., Isfahan Univ. of Technol., Isfahan, Iran

fYear

2013

fDate

14-16 May 2013

Firstpage

1

Lastpage

6

Abstract

A hybrid neuro-fuzzy system based on interval type-2 fuzzy c-means clustering, MLP neural network and interval type-2 fuzzy model is proposed for predicting the noisy forex market. To gain faster convergence in learning procedure, combination of back-resilient and back-propagation is used. Two EURUSD and USDCHF exchange rates from forex market are used for experiments. The model is tested for convergence speed and one day ahead prediction. It is also compared with its fuzzy c-means based type-1 equivalent and a FLANN based neuro-fuzzy system. The performance of proposed model in convergence speed and prediction accuracy is proved by experimental results.

Keywords

backpropagation; exchange rates; fuzzy set theory; multilayer perceptrons; pattern clustering; EURUSD exchange rates; FLANN based neuro-fuzzy system; IT2 FCM; IT2 neuro-fuzzy system; MLP neural network; USDCHF exchange rates; back-propagation; back-resilient; convergence speed; foreign exchange rate forecasting; forex market; fuzzy c-means based type-1 equivalent; hybrid neuro-fuzzy system; interval type-2 fuzzy c-means clustering; interval type-2 fuzzy model; learning procedure; Clustering algorithms; Computational modeling; Convergence; Fuzzy systems; Neural networks; Prototypes; Uncertainty; IT2 fuzzy c-means; Interval type-2 fuzzy; Neuro-fuzzy system;

fLanguage

English

Publisher

ieee

Conference_Titel

Electrical Engineering (ICEE), 2013 21st Iranian Conference on

Conference_Location

Mashhad

Type

conf

DOI

10.1109/IranianCEE.2013.6599870

Filename

6599870