摘要
该文对钢结构企业的库存特性进行了分析研究,针对其特点提出了安全库存的重要性,然后指出了传统的安全库存预测方法的不足。以某钢构企业的8个影响安全库存的主要因素为例,研究了遗传算法优化BP神经网络权值的方法,最终证明优化后的网络模型具备较好的精度。
We first analyzed the inventory characteristics of steel structure enterprise,aiming to put forward the importance of safety stock. And then points out the shortage of traditional prediction method of safety stock. In the eight main factors affecting safety stock for example of some steel structure enterprise ,we studied the method of using genetic algorithm to optimize the weights of BP neural network. And ultimately proved that the optimized network mod- el has better precision.
出处
《自动化与仪表》
2015年第8期5-8,共4页
Automation & Instrumentation
基金
天津市高等学校科技发展基金计划项目(20120814)
河北省科技支撑计划项目(13210307D)
关键词
预测
安全库存
BP神经网络
遗传算法
prediction
safety stock
BP neural network
genetic algorithm
