摘要
为解决现在物流供应链价格预测模型存在的预测效果低、预测精度差的问题,将灰色预测算法与模糊神经网络算法进行融合,并基于融合后的算法构建物流供应链价格预测模型,以期提高供应链价格预测的准确率。对不同模型性能进行对比实验,发现融合算法的计算速度最快,平均值为8.3 bps。而所构建模型对供应链中各项价格的预测准确率均高于95%,表明所提出的融合算法能够提高物流供应链价格预测的准确率,以此降低供应链成本。
To address the issues of low prediction effect and poor prediction accuracy in current logistics supply chain price prediction models,this study integrates the grey prediction algorithm with the fuzzy neural network algorithm and builds a logistics supply chain price prediction model based on the integrated algorithm,aiming to enhance the accuracy of supply chain price prediction.Comparative experiments on the performance of different models were conducted,revealing that the integrated algorithm has the fastest calculation speed,with an average of 8.3 bps.Moreover,the prediction accuracy of the constructed model for various prices in the supply chain is all above 95%.This indicates that the proposed integrated algorithm can improve the accuracy of logistics supply chain price prediction,thereby reducing supply chain costs.
作者
李亮亮
LI Liang-liang(School of Economics and Trade,Anhui Business and Technology College,Anhui,Hefei 231131)
出处
《贵阳学院学报(自然科学版)》
2025年第2期95-100,共6页
Journal of Guiyang University:Natural Sciences
基金
安徽省高校人文社科重点项目(SK2024A004)
安徽省高校教学研究项目(2023xjjy33)。
关键词
GM算法
模糊神经网络
物流供应链
价格预测
GM algorithm
Fuzzy neural network
Logistics supply chain
Price prediction
作者简介
李亮亮,女,讲师、硕士。研究方向:经济贸易。
