统计与决策2023，Vol.39Issue(24)：23-28,6.DOI:10.13546/j.cnki.tjyjc.2023.24.004
基于贝叶斯支持向量回归机的稳健参数设计
Robust Parameter Design Based on Bayesian Support Vector Regression Machine
摘要
Abstract
Robust parameter design is an important technique for quality improvement,which can be used to reduce and con-trol fluctuations from the source of production.The dual response surface method is a commonly used one.It mainly uses a low-or-der polynomial model to fit the mean and variance responses.However,when the sample is complex(such as nonlinear or high-di-mensional samples),the fitting performance of the low-order polynomial model is often worse,and the solution to the optimization problem is not effective.Support vector regression machine has good fitting potential to nonlinear data,but its performance de-pends on the reasonable setting of parameters.This paper applies Bayesian optimization to parameter selection of support vector regression machine,then uses the optimized model for the construction of response surface model in robust parameter design,and finally proposes a robust parameter design method based on Bayesian support vector regression machine.The experimental results show that the proposed method can be used to obtain more accurate response surfaces than other common optimization methods,capable of approximating reliable optimal factor collocation levels in practical applications.关键词
稳健参数设计/支持向量回归机/贝叶斯优化Key words
robust parameter design/support vector regression machine/Bayesian optimization分类
数理科学引用本文复制引用
周晓剑,顾翔..基于贝叶斯支持向量回归机的稳健参数设计[J].统计与决策,2023,39(24):23-28,6.基金项目
国家自然科学基金资助项目(71872088) （71872088）
