基于BP神经网络的辽宁省物流需求预测研究

物流需求预测对经济发展具有重要作用。选取辽宁省2004—2021年的7个经济指标影响因素作为输入指标，货物运输量作为物流需求的输出指标，利用MATLAB R2022b软件，对辽宁省物流需求进行预测。利用灰色关联度分析法，对经济指标影响因素的关联度进行分析。结果认为，输入指标与输出指标具有较强关联度。随后，基于BP神经网络法构建物流需求预测模型，经过仿真预测，BP神经网络模型对物流需求预测具有有效性。

Research on Logistics Demand Prediction in Liaoning Province Based on BP Neural Network

Logistics demand forecasting plays an important role in economic development. Selecting 7 economic indicators influencing factors from 2004 to 2021 in Liaoning Province as input indicators, and cargo transportation volume as output indicators of logistics demand, MATLAB R2022b software was used to predict logistics demand in Liaoning Province. Using the grey correlation analysis method, the correlation degree of the influencing factors of economic indicators was analyzed. Based on the results, it is believed that there is a strong correlation between input indicators and output indicators. Subsequently, a logistics demand prediction model was constructed based on the BP neural network method. After simulation and prediction, it is found that the BP neural network model is effective in predicting logistics demand.