摘要
为克服传统商品库存成本过大和消费者满意度过低的弊端,采用BP神经网络方法,以超市一段时间内的销售记录为样本数据,分析BP神经网络库存控制模型的训练过程,并验证BP神经网络的自适应能力、容错能力以及处理非线性关系的能力,保证库存预测的准确性,最终提出基于BP神经网络算法的商品库存控制模型.研究结果表明:该控制模型能够准确高效控制超市商品库存,可以为合理控制库存提供决策支持,有效提高库存控制的效率.
In order to overcome the drawbacks of the high traditional inventory costs and low consumer satisfaction, this paper analyzed the training process of BP neural network models of inventory control based on the sales record of a supermarket during a period of time as sample data, and verified that the BP neural network adaptive ability, fault-tolerant ability and the ability of dealing with the nonlinear relationship, ensured the accuracy of inventory forecast, finally proposed the inventory control model based on BP neural network algorithm. The results show that the control model can accurately control the supermarket merchandise inventory, provide decision support for reasonable control of inventory, and improve the efficiency of inventory control.
出处
《辽宁工程技术大学学报（自然科学版）》
CAS
北大核心
2013年第6期817-821,共5页
Journal of Liaoning Technical University (Natural Science)
关键词
超市
BP神经网络
数据挖掘
库存控制
最小化
预测
信息化
决策支持
supermarket
BP neural network
data mining
inventory control
minimize
forecast
informatization
decision support
作者简介
孔繁烨（1983-），男，辽宁大连人，硕士研究生，主要从事企业信息化及计算机应用技术方面的研究．
