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自适应差分进化算法优化BP神经网络的时间序列预测
引用本文:王林,彭璐,夏德,曾奕. 自适应差分进化算法优化BP神经网络的时间序列预测[J]. 计算机工程与科学, 2015, 37(12): 2270-2275
作者姓名:王林  彭璐  夏德  曾奕
作者单位:;1.华中科技大学管理学院;2.武汉理工大学管理学院
基金项目:中央高校基本科研业务费资助项目(HUST:2014QN201)
摘    要:针对BP神经网络学习算法随机初始化连接权值和阈值易使模型陷入局部极小点的缺点,设计了一种自适应差分进化算法优化BP神经网络的混合算法。该混合算法中,差分进化算法采用自适应变异和交叉因子优化BP神经网络的初始权值和阈值,再用预寻优得到的初始权值和阈值训练BP神经网络得到最优的权值和阈值。首先对改进的自适应差分进化算法运用测试函数进行性能测试,然后用一个经典时间序列问题对提出的混合算法进行了检验,并与一般的神经网络、ARIMA预测模型及其它混合预测模型进行了对比,实验结果表明,本文提出的混合算法有效并且明显提高了预测精度。

关 键 词:时间序列预测  BP神经网络  差分进化算法
收稿时间:2014-12-16
修稿时间:2015-12-25

BP neural network incorporating self-adaptive differential evolution algorithm for time series forecasting
WANG Lin,PENG Lu,XIA De,ZENG Yi. BP neural network incorporating self-adaptive differential evolution algorithm for time series forecasting[J]. Computer Engineering & Science, 2015, 37(12): 2270-2275
Authors:WANG Lin  PENG Lu  XIA De  ZENG Yi
Affiliation:(1.School of Management,Huazhong University of Science and Technology,Wuhan 430074;2.School of Management,Wuhan University of Technology,Wuhan 430070,China)
Abstract:It is easy for a BP neural network (BP NN) to be trapped into a local minimum point for the time series forecasting problem. To improve the forecasting accuracy, we design a hybrid algorithm which combines the self adaptive differential evolution algorithm (SDE) with the BP NN. We adopt the SDE algorithm to search for global initial weights and thresholds of the BP NN. These values are then employed to further search for the optimal weights and thresholds. The performance of the proposed SDE algorithm is verified through benchmark functions and a well known real data set is used to verify the effectiveness of the hybrid algorithm. Compared with general neural network, ARIMA and other hybrid models,experimental results indicate that the proposed algorithm can be an effective way to improve forecasting accuracy.
Keywords:time series forecasting  BP neural network  differential evolution algorithm,
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