Traffic Flow Prediction Algorithm Study via Chaos RBF Network
Zhang Li-dong · Journal of University of Jinan · 2012
To improve the precision of traffic flow forecasting,we studied a kind of new chaos RBF neural network(C-RBF) algorithm.It first calculates embedding dimensions and embedding delay of chaos phase space,takes the phase space vector as the input of RBF neural network and the vector value next to input as the output expectation value,finally trains the network and gets the weights.In practice,the collected traffic flow could be input into the net,and after phase space reconstruction and network forecasting,the prediction and error value are got.The simulation results show that our algorithm is much more precise than common RBF network,and the prediction accuracy is improved by 96%.