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To accurately and efficiently predict regional logistics requirements, a prediction model based on BP-RBF neural network is proposed. Considering the characteristics of nonlinear changes in logistics demand, in the modeling process, BP and RBF are first used to establish a single prediction submodel respectively, and then the weighted combination prediction model is further constructed, and finally the empirical analysis of logistics demand prediction in Shanghai is conducted. From the results it is clear that the combined logistics demand prediction model will have higher prediction accuracy for a single logistics demand prediction model, which greatly reduces the possibility of error, makes the prediction results more reasonable and accurate, and can provide help for the planning after regional logistics.
Yujia Ren (Fri,) studied this question.