Forecasting the RMB Exchange Regime

Abstract

To resolve the slow convergence and local minimum problem of BP network, an exchange rate forecast method based on Radial Basis Function Neural Network (RBFNN) is proposed. Data on economic variables is normalized, and then is put into the RBFNN in training. Corresponding parameters are got and then the exchange rate is predicted. Detailed simulation results and comparisons with Back-Propagation (BP) network show that, the operation speed of the method is faster and the forecast accuracy is higher than the traditional BP neural network can be achieved obviously. We then use genetic programming approach to achieve a better outcome compared with ANN.