Evolutionary functional link interval type-2 fuzzy neural system for exchange rate prediction

Abstract: This paper proposes a hybrid evolutionary functional link and interval type-2 fuzzy neural system (EFLIT2FNS) to predict the currency exchange rate data for six different time horizons starting from one day to one year. The antecedent part of each fuzzy rule of EFLIT2FNS model is an interval type-2 fuzzy set and the fuzzy rules are of the Takagi-Sugano-Kang (TSK) and the consequent part comprises a functional link artificial neural network (FLANN). The parameters of both the antecedent and consequent part of the fuzzy rules are optimised by the gradient descent algorithm. Further, to overcome the limitations of the above said algorithm, two evolutionary algorithms, i.e., genetic algorithm (GA) and differential evolution (DE) are used to optimise all the parameters used in the model. Five different currency exchange time series data are considered for evaluating the performance of the proposed algorithm. The simulation results reveal that the prediction accuracy of the EFLIT2FNS model is significantly better than other models used for comparison.