基于神经网络分析的北京城镇农产品冷链物流需求预测

Forecast of cold chain logistics demand for agricultural products in Beijing based on neural network

为了预测由居民消费所引起的农产品冷链物流需求,从农产品供给、社会经济、冷链发展、人文发展、物流需求规模5个角度构建影响指标体系,利用BP神经网络和RBF神经网络在需求预测中的优势,建立基于主成分分析和神经网络组合模型的需求预测模型,并以北京为例,对两种模型的预测结果进行对比分析。结果表明:通过灰色关联度分析各个影响指标,发现北京市城镇人口数量、第一产业增加值、第三产业占GDP比重等因素对农产品冷链物流需求量的影响最大;预测到2020年北京城镇居民农产品冷链物流需求将达到642.27万t;所建立的模型对冷链物流需求及其影响因素的非线性关系方面有较高的精度和应用价值,能够为农产品冷链物流规划者及政府提供定量决策依据。

In order to forecast the demand of agricultural products cold chain logistics caused by the consumption,this paper constructs an impact index system from six aspects of agricultural products supply,social economy,cold chain development,humanities,logistics demand scale,and uses the advantages of BP neural network and RBF neural network in demand forecasting,establishes a demand forecasting model based on principal component analysis and neural network combination model. This paper takes Bei Jing as an example,and compares and analyzes the forecasting results of the two models. The results show that ：through the grey relational analysis of various impact indicators,it is found that the number of urban population,the added value of the primary industry,the proportion of the tertiary industry to GDP and other factors have the greatest impact on the demand of agricultural products cold chain logistics;Forecast by 2020 Beijing urban residents agricultural products cold chain logistics demand will reach 6.642 million tons;The model established in this paper has high precision and application value in the non-linear relationship between cold chain logistics demand and its influencing factors,and can provide quantitative decision-making basis for agricultural cold chain logistics planners and governments.