徐晓燕,杨慧敏,吕修凯,王雪,康静彩.基于山东省不同模型的物流需求预测比较研究[J].包装工程,2022,43(23):207-215.
XU Xiao-yan,YANG Hui-min,LYU Xiu-kai,WANG Xue,KANG Jing-cai.Comparative Research on Forecast of Logistics Demand in Shandong Province Based on Different Models[J].Packaging Engineering,2022,43(23):207-215. |
| 基于山东省不同模型的物流需求预测比较研究 |
| Comparative Research on Forecast of Logistics Demand in Shandong Province Based on Different Models |
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| DOI：10.19554/j.cnki.1001-3563.2022.23.025 |
| 中文关键词: 小波神经网络 人工神经网络 遗传算法优化神经网络 粒子群优化神经网络 长短时记忆网络 需求预测 |
| 英文关键词: wavelet neural network BP neural network BP neural network by genetic algorithm BP neural network by particle swarm long short-term memory demand forecast |
| 基金项目:中央高校业务经费(2572016CB11);校级教育教学研究项目(DGY2020–42) |
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| 目的 过对不同预测方法的误差对比研究，选取预测生鲜农产品物流需求量更精准方法，为疫情情况下山东省生鲜农产品市场进行科学性、合理化决策提供参考。方法 公路货物周转量、互联网普及率、GDP、人口数量、第一产业增加值等十大影响因素作为自变量，以生鲜农产品的需求量作为因变量，分别将小波神经网络、人工神经网络(BP)、遗传算法优化神经网络(GA−BP)、粒子群优化神经网络(PSO−BP)、长短时记忆网络(LSTM)等5种方法的数据预测进行对比分析。结果 波神经网络和BP神经网络的预测值明显低于真实值，且平均相对误差接近20%，而优化后的GA−BP、PSO−BP、LSTM算法误差均小于5%，分别为4.06%、1.162%、0.45%，因此，LSTM预测精度最高，效果最好。结论 来山东省的生鲜农产品需求量将持续增长，LSTM算法以其精确度更高，学习能力更强的优点，将会被更多地应用到物流领域研究中。 |
| 英文摘要: |
| The work aims to compare and study the errors of different prediction methods to select a more accurate method for predicting the logistics demand of fresh agricultural products, and provide a reference for scientific and rational decision-making in the fresh agricultural product market in Shandong Province under the epidemic situation. With ten influencing factors, such as highway cargo turnover, Internet penetration rate, GDP, population, and added value of the primary industry, as independent variables, and the demand for fresh agricultural products as the dependent variable, the data prediction of five methods such as wavelet neural network, BP neural network, BP neural network by genetic algorithm (GA-BP), BP neural network by particle swarm (PSO-BP), long short-term memory (LSTM)were compared and analyzed. The predicted values of wavelet neural network and BP neural network were obviously lower than the actual values, and the average relative error was close to 20%, while the errors of optimized GA−BP, PSO−BP and LSTM algorithms were all less than 5%, which were 4.06%, 1.162% and 0.45% respectively. Therefore, LSTM had the highest prediction accuracy and the best effect. In the future, the demand for fresh agricultural products in Shandong Province will continue to grow, and the LSTM algorithm will be more applied in the field of logistics research due to its advantages of higher accuracy and stronger learning ability. |
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