摘要
以模具制造企业中的备件管理系统为研究背景,重点分析模具制造企业备件的主要特征,结合传统ABC分类法和BP神经网络的基本原理,提出了基于BP神经网络算法的备件ABCD分类法,通过对样本数据进行多次网络训练,最终确定理想分类模型,同时对此分类模型进行精度检验,证实其精度达到预期目标。
Based on the spare parts management system of mold manufacturing enterprises,the spare parts of main features of the mold manufacturing enterprises were analyzed,combining the traditional ABC classification method and the basic principle of BP neural network,and the spare parts＇ABCD classification method which came from BP neural network algorithm were proposed.Through the repetitious network training on the sample data,an ideal classified model was established.At the same time,the accuracy of the model was tested with the test samples and confirmed that the reliability had reached to the prospective goals.
出处
《机械设计与制造》
北大核心
2012年第11期236-238,共3页
Machinery Design & Manufacture
基金
广东省教育部产学研合作项目(2010B09l101007)
广东省科技计划项目(2009A010200010)
关键词
模具制造
备件
神经网络
分类模型
Mold Manufacturing
Spare Parts
Neural Network
Classification Model
