Computer Engineering and Applications ›› 2009, Vol. 45 ›› Issue (24): 210-212.DOI: 10.3778/j.issn.1002-8331.2009.24.063

• 工程与应用 • Previous Articles     Next Articles

Research of exchange rate forecast model based on Radial Basis Function neural network

LIAO Wei1,FENG Xiao-bing2,XU Chun-dong3,LIU Jin-gao1   

  1. 1.Department of Information Science and Technology,East China Normal University,Shanghai 200241,China
    2.Shanghai Academy of Social Science,Jian Qiao College,Shanghai 200200,China
    3.School of Information Science and Technology,Jiangxi University of Technology,Ganzhou,Jiangxi 341000,China
  • Received:2009-04-17 Revised:2009-06-15 Online:2009-08-21 Published:2009-08-21
  • Contact: LIAO Wei

径向基神经网络的汇率预测模型研究

廖 薇1,冯小兵2,许春冬3,刘锦高1   

  1. 1.华东师范大学 信息学院 电子系,上海 200241
    2.上海社科院 世界经济和政治研究所,上海建桥学院,上海 200200
    3.江西理工大学 信息工程学院,江西 赣州 341000
  • 通讯作者: 廖 薇

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.

Key words: Radial Basis Function(RBF), neural network, exchange rate, forecast

摘要: 针对BP网络存在着收敛速度慢和局部极小的问题,提出了一种基于径向基神经网络的汇率预测研究方法。将经济变量数据归一化处理,然后送入径向基神经网络(RBF)中训练,得出相应参数,再对汇率进行预测。详细的仿真实验以及与BP神经网络的比较表明,该方法不仅运算速度较快,且预测精度明显要高于传统BP神经网络所能达到的效果。

关键词: 径向基函数, 神经网络, 汇率, 预测

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