Traditional BP algorithm has defects of local minima and slow convergence. Based on application for exchange rate forecasting, this paper gives an improved BP neural network activation function. Then propose a new activation function after by analyze influence factor of the exchange rate and design structure of forecasting of the improved BP model. Take simulation forecast for a sample data of the RMB exchange rate. The results show that the improved BP neural network not only has accelerated in the training speed, also has a significant improvement in forecasting performance compare to the traditional BP neural network. © 2012 Springer-Verlag GmbH.
CITATION STYLE
Chun-Dong, X., Wei, L., Mu-Gui, Z., & Jin-Gao, L. (2012). A BP neural network activation function used in exchange rate forecasting. In Advances in Intelligent and Soft Computing (Vol. 134 AISC, pp. 69–76). https://doi.org/10.1007/978-3-642-27537-1_10
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