Multi-step Prediction Algorithm of Traffic Flow Chaotic Time Series Based on Volterra Neural Network

Abstract

The accurate traffic flow time series prediction is the prerequisite for achieving traffic flow inducible system. Aiming at the issue about multi-step prediction traffic flow chaotic time series, the traffic flow Volterra Neural Network (VNN) rapid learning algorithm is proposed. Combing with the chaos theory and the Volterra functional analysis, method of the truncation order and the truncation items is given and the VNN model of traffic flow time series is built. Then the mechanism of the chaotic learning algorithm is described, and the adaptive learning algorithm of VNN for traffic flow time series is designed. Last, a multi-step prediction of traffic flow chaotic time series is researched by traffic flow VNN network model, Volterra prediction filter and the BP neural network based on chaotic algorithm. The simulations show that the VNNTF network model predictive performance is better than the Volterra prediction filter and the BP neural network by the simulation results and root-mean-square value.