Wavelet-Based Elman Neural Network with the Modified Differential Evolution Algorithm for Forecasting Foreign Exchange Rates  

作　　者：Renquan HUANG Jing TIAN 

机构地区：[1]School of Economics and Finance,Xi’an International Studies University,Xi’an 710128 China

出　　处：《Journal of Systems Science and Information》2021年第4期421-439,共19页系统科学与信息学报（英文）

基　　金：Supported by National Natural Science Foundation of China(61402364);Soft Science Research Project of Xi’an Science and Technology Plan(XA2020-RKXYJ-0075);Xi’an International Studies University Research Foundation(19XWB06)。

摘　　要：It is challenging to forecast foreign exchange rates due to the non-linear characters of the data. This paper applied a wavelet-based Elman neural network with the modified differential evolution algorithm to forecast foreign exchange rates. Elman neural network has dynamic characters because of the context layer in the structure. It makes Elman neural network suit for time series problems. The main factors, which affect the accuracy of the Elman neural network, included the transfer functions of the hidden layer and the parameters of the neural network. We applied the wavelet function to replace the sigmoid function in the hidden layer of the Elman neural network, and we found there was a "disruption problem" caused by the non-linear performance of the wavelet function. It didn’t improve the performance of the Elman neural network, but made it get worse in reverse. Then, the modified differential evolution algorithm was applied to train the parameters of the Elman neural network. To improve the optimizing performance of the differential evolution algorithm, the crossover probability and crossover factor were modified with adaptive strategies, and the local enhanced operator was added to the algorithm. According to the experiment, the modified algorithm improved the performance of the Elman neural network, and it solved the "disruption problem" of applying the wavelet function.These results show that the performance of the Elman neural network would be improved if both of the wavelet function and the modified differential evolution algorithm were applied integratedly.

关 键 词：dividend bonus normal distribution function differentiated taxation 

分 类 号：F832.6[经济管理—金融学] F224