Emad Fallahzadeh - Isfahan University of Technology
Mohammad Ali Montazeri

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

Neuro-fuzzy system, Interval type-2 fuzzy, IT2 fuzzy c-means