基于模糊认知图的物流需求预测模型研究

准确地预测社会物流需求,在政府对物流行业政策制定、企业物流活动规划中有着重要意义.本文提出一种基于模糊认知图的物流需求预测模型构建方法,综合考虑国内生产总值、进出口总额等五个经济要素与物流需求之间的相互影响关系,通过对历史数据机器学习获得相互影响权重,构建了物流需求预测模型,可对未来物流需求进行推算和预测.实验证明,该模型对物流需求的预测精度较高,效果较好.

Study on logistics demand forecasting model based on fuzzy cognitive map

Accurate prediction of social logistics demands is essential to government's policy formulation for the logistics industry as well as to enterprise's logistics activity planning. In this paper, a logistics demand prediction model construction method based on the fuzzy cognitive map (FCM) is proposed. This method comprehensively considers the mutual influence between five economic elements (GDP, total import and export volume, etc.) and logistics demands, and acquires the mutual influence weight through machine learning of historical data. Finally, a logistics demand prediction model is built, which can realize accurate prediction of the future logistics demands. The experimental results provide solid evidence for high precision and favorable performance of the model in predicting logistics demands.