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中文摘要：
采用基于混沌动力系统的相空间重构和非线性系统的Volterra级数展开式,构建了交通流量的Volterra自适应预测模型。其基本思想是首先采用Lyapunov指数判定交通流时间序列存在混沌的前提下,对该时间序列进行相空间重构;然后选择Volterra级数构造非线性预测模型,并采用LMS类型的自适应算法来实时调整模型的系数。应用该模型对Lorenz、Rossler和交通流时间序列进行仿真研究。结果表明,提出的Volterra自适应预测模型能有效地预测低维混沌时间序列和交通流时间序列。
英文摘要：
This paper constructed an adaptive Volterra prediction model for traffic flow series,which was based on phase space reconstruction of chaos dynamic system and Volterra series for nonlinear system. On the premise that chaos existed in traffic flow time series by using Lyapunov exponent,performed phase space reconstruction for traffic flow data. Constructed nonlinear pre-diction model by applying Volterra series. The LMS-type adaptive algorithm,which was derived from least square error,used to update this model＇s coefficients. Finally,applied this Volterra prediction model to performing simulations for chaotic time series generated by Lorenz and Rossler and traffic flow time series data. Experimental results show that the proposed adaptive Volterra prediction model is capable of effectively predicting low-dimension chaotic time series and traffic flow time series.
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