兵工自动化2026，Vol.45Issue(6)：26-30,43,6.DOI:10.7690/bgzdh.2026.06.006
基于贝叶斯-支持向量回归的压装药密度预测算法
Prediction Algorithm of Charge Density Based on Bayesian Optimization-support Vector Regression
赵维亮 1张雷 2庄存波 1袁申 3黄求安 3张旭1
作者信息
- 1. 北京理工大学,北京 100081
- 2. 天津商业大学,天津 300134
- 3. 中国兵器装备集团自动化研究所有限公司智能制造事业部,四川 绵阳 621000
- 折叠
摘要
Abstract
A Bayesian optimization-support vector regression(BO-SVR)algorithm is proposed for the problem of predicting the molding density of press-loaded drugs.The orthogonal experimental method is used to collect the press-loading process parameters and quality data,and the data samples are analyzed for correlation,on the basis of which the support vector regression model is constructed,and the Bayesian algorithm is used to search for the optimal combinations of the penalty coefficients of the support vector regression model as well as hyperparameters such as the kernel function parameter,to evaluate the effects of the different combinations of the parameters,and to compare and analyze the effect of the BO-SVR model with that of the traditional support vector regression(SVR)model.The results show that BO-SVR nearly doubles all the evaluation indexes than the traditional SVR model.关键词
支持向量回归/贝叶斯优化算法/交叉验证/参数优化/质量预测Key words
support vector regression/Bayesian optimization algorithm/cross-validation/parameter optimization/quality prediction分类
军事科技引用本文复制引用
赵维亮,张雷,庄存波,袁申,黄求安,张旭..基于贝叶斯-支持向量回归的压装药密度预测算法[J].兵工自动化,2026,45(6):26-30,43,6.
