交通流序列的Volterra自适应预测

基于混沌动力系统的相空间重构和非线性系统的Volterra级数,构建交通流的Volterra自适应预测模型.在应用小数据量法判定交通流存在混沌特性的前提下,分别用平均互信息法和虚假邻点法选取延滞时间和嵌入维数以实现对交通流时间序列的相空间重构.通过Volterra级数展开式建立非线性预测模型,采用LMS自适应算法实时调...

Volterra Adaptive Prediction of Traffic Flow Sequence

Based on phase space reconstruction of chaos dynamic system and Volterra series for nonlinear system, Volterra adaptive prediction model for traffic flow is constructed. On the premise that small data set method is used to determine that chaos exists in traffic flow time series, this paper respectively employs average mutual information method and false nearest neighbor technique to choose delay time and embedding dimension so as to perform phase space reconstruction for traffic flow data. Nonlinear prediction model, whose coefficients are real-time updated by LMS adaptive algorithm is constructed by applying Volterra series extensions. It applies this Volterra prediction model to performing simulations for the real measured expressway traffic flow data and chaotic time series generated by Chens and Duffing. Experimental results show that the proposed Volterra adaptive prediction model is capable of effectively predicting traffic flow time sequence and low-dimensional chaotic time sequence.