径向基神经网络的汇率预测模型研究

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

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。

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.