Prediction of exchange rate using improved particle swarm optimised radial basis function networks

In this paper, a radial basis function neural network (RBFN) model has been trained by canonical particle swarm optimisation (PSO) and improved particle swarm optimisation (IMPSO) algorithms to efficiently predict the exchange rate of Indian rupees against the exchange rate of G-7 countries for future days. We have used two variants of PSO such as canonical PSO and IMPSO for optimising the parameters of radial basis function neural network through learning from the past data of exchange rate prediction. Here, we have considered 43 countries' exchange rates to predict the Indian rupees against the G-7 countries. Forty-three exchange rates have been collected and based on their correlation analysis a dataset has been prepared to validate the proposed model. In addition, a fair comparison has been carried out between IMPSO tuned RBFN and canonical PSO tuned RBFN with respect to the results obtained by varying the number of iterations for future days' prediction. From the experimental results, it is observed that the predictive performance of IMPSO tuned RBFN modelling the case of higher number of iterations is promising vis-à-vis canonical PSO tuned RBFNs model.