兵工自动化2025，Vol.44Issue(4)：26-31,6.DOI:10.7690/bgzdh.2025.04.006
基于LSTM和贝叶斯网络的枪械交验合格率预测
Prediction of Firearms Acceptance Rate Based on LSTM and Bayesian Network
王宪升 1胡瑶 1姜黎明 1郝佳 2孙嘉伟 2张晓宁 2陈东阳1
作者信息
- 1. 重庆建设工业(集团)有限责任公司工艺技术研究所,重庆 400054
- 2. 北京理工大学机械与车辆学院,北京 100081
- 折叠
摘要
Abstract
In order to accurately locate the key processing links affecting the pass rate of the first delivery of the finished gun,the Bayesian network is selected to construct a causal model between the processing parameters and the pass rate.By selecting the long short-term memory(LSTM)neural network model as the time series prediction model for the pass rate of the first delivery of guns,the pass rate of the first delivery of guns in the next batch can be predicted more accurately,and the key processing links can be further located.The results show that the prediction can provide a theoretical reference for the next targeted improvement of the production process.关键词
交验合格率/预测/LSTM模型/贝叶斯网络Key words
acceptance rate/prediction/LSTM model/Bayesian network分类
武器工业引用本文复制引用
王宪升,胡瑶,姜黎明,郝佳,孙嘉伟,张晓宁,陈东阳..基于LSTM和贝叶斯网络的枪械交验合格率预测[J].兵工自动化,2025,44(4):26-31,6.
