Prediction of Short-Term Traffic Flow Based on PSO-Optimized Chaotic BP Neural Network

In order to improve the prediction accuracy of BP neural network model for chaotic time series, a new prediction method for chaotic time series of optimized BP neural network based on particle swarm optimization (PSO) was presented. The PSO was used to optimize the initial weights and thresholds of BP neural network, and then the BP neural network was trained to search for the optimal solution. The efficiency of the proposed prediction method was tested by simulation of several typical nonlinear systems and time series of real traffic flow. The simulation results have shown that the better fitting and higher accuracy are expressed in this improved method.