Regional Logistics Demand Forecasting: A Method Based on Improved Grey Neural Network
Guest Editor Yuantao Teng (Jilin Normal University, Shiping City, China)
Regional logistics demand prediction directly affects regional economy development and logistics planning. Grey prediction, BP neural network, grey neural network and other methods have been widely used in the prediction of complex nonlinear logistics demand system. In this study, an improved grey neural network model was proposed based on these models. First, in this model, the grey influence factors and the grey targets were completely taken as inputs, and the actual targets were taken as outputs. Then, the weights of neural network were optimized by continuous positive input and feedback adjustment to improve the prediction accuracy. Finally, an experiment was conducted to verify the effectiveness of the proposed model by analyzing the historical logistics demand data of Hubei province. The results show that the improved grey neural network is feasible and effective. The experiment results demonstrate the promising application of the proposed method in regional logistics demand forecasting.
Journal: International Journal of Simulation: Systems, Science and Technology, IJSSST V17
Published: no date/time given