Rough Orthogonal Wavelet Network and Its Applications to the Traffic Flow Forecast
粗正交小波网络及其在交通流预测中的应用

Based upon the characteristics of traffic flow forecast and the principal component analysis (PCA), a new traffic flow forecasting model combined rough sets and orthogonal wavelet network together, called rough orthogonal wavelet network forecasting model, is put forward. The model has been successfully used to forecast urban traffic flow. Using the principal component analysis about input vectors, the model keeps away from the dimension avalanche of orthogonal wavelet networks. The experiment results show that the model is superior to the BP networks and wavelet frame networks in the aspects of flow forecasting precision and network convergence. The rough neural network forecasting model is robust to the uncertain factors for the traffic flow forecast. The model given in this paper is of academic and practical value in forecasting applications.