An Improved Phase Space Reconstruction Method-Based Hybrid Model for Chaotic Traffic Flow Prediction

Chaotic Prediction for Traffic Flow of Improved BP Neural Network

Abstract A prediction algorithm for traffic flow prediction of BP neural based on Differential Evolution (DE) is proposed to overcome the problems such as long computing time and easy to fall into local minimum by combing DE and neural network. In the algorithm, DE is used to optimize the thresholds and weights of BP neural network, and the BP neural network is used to search for the optimal so...

Short-term Traffic Flow Prediction Based on Multivariable Phase Space Reconstruction and LSSVM

Real-time and accurate short-term traffic flow prediction is the premise and key of intelligent traffic control and guidance system. According to this problem, this paper put forward a prediction model based on multivariable phase space reconstruction and least squares support vector machine (LSSVM). First, the model confirms embedding dimension and delay time of the traffic flow, occupancy and...

Adaptive Chaotic Prediction Algorithm of RBF Neural Network Filtering Model Based on Phase Space Reconstruction

With the analysis of the technology of phase space reconstruction, a modeling and forecasting technique based on the Radial Basis Function (RBF) neural network for chaotic time series is presented in this paper. The predictive model of chaotic time series is established with the adaptive RBF neural networks and the steps of the chaotic learning algorithm with adaptive RBF neural networks are ex...

Phase-space reconstruction of an atomic chaotic system

Improved Hybrid Intelligent Method for Urban Road Traffic Flow Forecasting based on Chaos-PSO Optimization

Real time traffic flow is often difficult to predict precisely because of the nonlinear and stochastic characteristics of the traffic flow data. Intelligent prediction methods such as artificial neural network (ANN), support vector machine (SVM), etc. have been proven effective to discover the nonlinear information hidden in the traffic flow data. Nevertheless, their efficiency limits in the lo...