Research on the Prediction of FIR Traffic Flow Neural Network Based on Chaotic Adaptive Algorithm

摘要

By researching the modeling basis on traffic flow chaotic time series and FIR networks, the FIRTF model is proposed to make prediction of traffic flow series. It is proposed that the gray correlation analysis method to determining the number of hidden layer neurons, in the circumstances to vector signals to the FIRIF neural networks hidden layer neuron signals. Based on FIR networks learning algorithm, the FIRTF networks adaptive chaos mechanism algorithm is designed. At last, by the simulation results and root-mean-square value, the traffic FIRTF neural networks learning and prediction owned on a better advantages, because that the traffic flow FIRTF and the adaptive algorithm combination integrated gda-BP networks, gdm-BP networks, gda-feedback-BP networks and gdm-feedback-BP networks of the four characteristics.