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| Forecast of cold chain logistics demand for agricultural products in Beijing based on neural network |
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| DOI：10.16768/j.issn.1004-874X.2018.06.020 |
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| Abstract: |
| 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. |
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