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交通流量VNNTF神经网络模型多步预测研究

殷礼胜 何怡刚 董学平 鲁照权

殷礼胜, 何怡刚, 董学平, 鲁照权. 交通流量VNNTF神经网络模型多步预测研究. 自动化学报, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066
引用本文: 殷礼胜, 何怡刚, 董学平, 鲁照权. 交通流量VNNTF神经网络模型多步预测研究. 自动化学报, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066
YIN Li-Sheng, HE Yi-Gang, DONG Xue-Ping, LU Zhao-Quan. Research on the Multi-step Prediction of Volterra Neural Network for Traffic Flow. ACTA AUTOMATICA SINICA, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066
Citation: YIN Li-Sheng, HE Yi-Gang, DONG Xue-Ping, LU Zhao-Quan. Research on the Multi-step Prediction of Volterra Neural Network for Traffic Flow. ACTA AUTOMATICA SINICA, 2014, 40(9): 2066-2072. doi: 10.3724/SP.J.1004.2014.02066

交通流量VNNTF神经网络模型多步预测研究

doi: 10.3724/SP.J.1004.2014.02066
基金项目: 

国家杰出青年科学基金(50925727),教育部科学技术研究重大项目(313018),安徽省高校自然科学基金重点项目(KJ2012A219),中国博士后科学基金(2013M541823)资助

详细信息
    作者简介:

    殷礼胜 博士,合肥工业大学电气与自动化工程学院副教授.2007年获得重庆大学控制科学与工程专业博士学位.主要研究方向为混沌理论,交通流,神经网络,现代智能算法.本文通信作者.E-mail:yls20000@163.com

    通讯作者:

    殷礼胜 博士,合肥工业大学电气与自动化工程学院副教授.2007年获得重庆大学控制科学与工程专业博士学位.主要研究方向为混沌理论,交通流,神经网络,现代智能算法.本文通信作者.E-mail:yls20000@163.com

Research on the Multi-step Prediction of Volterra Neural Network for Traffic Flow

Funds: 

Supported by National Natural Science Funds of China for Distinguished Young Scholar (50925727), (Key Grant) Project of Chinese Ministry of Education (313018), Natural Science Foundation of Univ ersities of Anhui Province (KJ2012A219), and China Postdoctoral Science Foundation (2013M541823)

  • 摘要: 研究了VNNTF 神经网络(Volterra neural network trafficflow model,VNNTF) 交通流量混沌时间序列多步预测问题. 通过分析比较交通流量混沌时间序列相空间重构的嵌入维数和Volterra 离散模型之间的关系,给出了确定交通流量Volterra 级数模型截断阶数和截断项数的方法,并在此基础上建立了VNNTF 神经网络交通流量时间序列模型;设计了交通流量Volterra 神经网络的快速学习算法;最后,利用交通流量混沌时间序列对VNNTF 网络模型,Volterra 预测滤波器和BP 网络进行了多步预测实验,比较了多步预测结果的仿真图、绝对误差的柱状图以及归一化后的方均根;实验结果表明VNNTF 神经网络的多步预测性能明显优于Volterra 预测滤波器和BP 神经网络.
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出版历程
  • 收稿日期:  2013-06-10
  • 修回日期:  2013-11-26
  • 刊出日期:  2014-09-20

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