Tianjin Port Throughput Prediction Based On Pca And Bp Neural Network

In view of the actual situation in the port of Tianjin, to overcome the traditional BP neural network method in the prediction model, such as low efficiency and poor accuracy of faults, the paper puts forward a kind of prediction model based on principal component analysis (PCA) and BP neural network. In this approach, use PCA to reduce the dimension of the original data, so as to achieve the purpose of simplifying the network structure. The calculation results of SPSS and MATLAB software show that PCA-BP neural network prediction model compared to traditional BP neural network model can improve the prediction efficiency and improve accuracy. Therefore, this method is more suitable for predicting the throughput of the port of Tianjin, and it has a strong practical significance.