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中国科学院大学学报 ›› 2015, Vol. 32 ›› Issue (3): 416-421.DOI: 10.7523/j.issn.2095-6134.2015.03.019

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DELTA势阱改进QPSO优化BP算法及其应用

于凤玲1, 周扬2, 陈建宏3, 周汉陵3   

  1. 1. 五邑大学经济管理学院, 广东 江门 529020;
    2. 湖南文理学院资源环境与旅游学院, 湖南 常德 415000;
    3. 中南大学资源与安全工程学院, 长沙 410083
  • 收稿日期:2013-10-16 修回日期:2014-07-28 发布日期:2015-05-15
  • 通讯作者: 于凤玲
  • 基金资助:

    国家自然科学基金(51374242)资助

BP neural network optimized with QPSO algorithm improved by DELTA potential trough and its application

YU Fengling1, ZHOU Yang2, CHEN Jianhong3, ZHOU Hanling3   

  1. 1. School of Economics & Management, Wu Yi University, Jiangmen 529020, Guangdong, China;
    2. College of Resources and Environment and Tourism, Hunan University of Arts and Science, Changde 415000, Hunan, China;
    3. School of Resources and Safety Engineering, Central South University, Changsha 410083, China
  • Received:2013-10-16 Revised:2014-07-28 Published:2015-05-15

摘要:

为了改进BP算法预测性能,提出QPSO-BP模型.该模型采用DELTA势阱改进的量子粒子群(QPSO)算法优化BP网络的权值与阈值,然后利用各年的GDP数据进行训练和预测.结果表明:经过DELTA势阱改进的QPSO优化BP算法模型比PSO-BP模型和BP神经网络更稳定,预测精度更高且泛化能力更强.与文献中所用模型的运算结果相比较,这种改进模型运算结果的相对误差和平均误差更小,在准确性上也有一定的优势.

关键词: BP神经网络, PSO模型, QPSO模型, &delta, 势阱, GDP

Abstract:

To improve the generalization ability of BP network for prediction, a BP neural network optimized with QPSO is proposed. This model uses the QPSO improved by δ potential trough to optimize the initial values of weights and thresholds of BP network. Then the data of each year's GDP are selected in training and prediction. The experiments show that the QPSO-BP network optimized by using δ potential trough produces stable prediction results. Compared with the prediction models of PSO-BP and BP, the proposed model has a better generalization ability and a higher accuracy. In addition, the calculation results of the improved QPSO-BP optimization algorithm model have smaller relative errors and average errors compared with the results of the models in the literature.

Key words: back-propagation neural network, PSO model, QPSO model, δ potential trough, GDP

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