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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, PP. 2066-2072

Keywords: 相空间重构,泛函级数,多步预测,VNN神经网络,算法,混沌

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Abstract:

?研究了VNNTF神经网络(Volterraneuralnetworktrafficflowmodel,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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