摘要
为了推动水产品冷链物流行业高质量发展,对水产品冷链物流需求量的精准预测是实现水产品冷链行业快速发展和物流资源合理配置的基础。针对目前冷链物流系统的复杂非线性,且统计数据样本量少的特征,提出了一种基于BP神经网络和支持向量机回归的组合预测模型。文章从区域经济、产品供给、冷链物流行业规模、社会四大维度选取10个指标构建影响因素指标体系,再结合各种预测方法的特点,选用BP-SVR组合预测模型。为验证该组合模型的性能,以湖北省2002-2021的相关数据进行仿真预测。结果表明,该组合预测模型平均相对误差仅为0.172,相比于单一的BP和SVR模型以及其他组合模型预测精度更高,因此使用BP-SVR组合预测模型能够为湖北省未来水产品的需求量提供一定的参考价值。
In order to promote the high-quality development of aquatic products cold chain logistics industry,accurate prediction of aquatic products cold chain logistics demand is the basis for realizing the rapid development of aquatic products cold chain industry and rational allocation of logistics resources.Aiming at the complex nonlinearity of the current cold chain logistics system and the small sample size of statistical data,a combined prediction model based on BP neural network and support vector machine regression is proposed.In this paper,ten indicators are selected from the four dimensions of regional economy,product supply,cold chain logistics industry scale,and society to construct the index system of influencing factors,and then combined with the characteristics of various prediction methods,the BP-SVR combined prediction model is selected.In order to verify the performance of the combined model,simulation prediction is carried out with the relevant data of Hubei Province from 2002 to 2021.The results show that the average relative error of the combined prediction model is only 0.172,which is higher than that of the single BP and SVR model and other combined models,so the use of the BP-SVR combined prediction model can provide a certain reference value for the future demand of aquatic products in Hubei Province.
作者
吴梦为
张洪
WU Mengwei;ZHANG Hong(School of Management,Wuhan University of Science and Technology,Wuhan 430065,China)
出处
《物流科技》
2024年第15期151-155,共5页
Logistics Sci-Tech
关键词
水产品
冷链物流
需求预测
BP-SVR组合模型
aquatic products
cold chain logistics
demand forecast
BP-SVR combination model
