摘要
针对模型不确定性条件下的稳健参数设计问题,在贝叶斯模型平均方法的基础上,通过考虑因子效应原则,提出基于因子效应原则的贝叶斯模型平均(BMA-EP)稳健性设计建模技术。结合先验信息与贝叶斯法则,计算主效应的后验概率并构建传统的贝叶斯模型平均模型;根据各主效应的后验概率,逐步运用效应层次原则和效应遗传原则更新各主效应的先验,确定模型的后验概率,并以该后验概率作为权重,对各模型进行加权,得到预测性能较佳且符合试验设计原则的稳健性模型。结合实际工业案例和仿真试验验证了所提方法的有效性,结果表明,所提方法不但改善了模型的预测性能,而且提高了最佳参数设置的可靠性。
Aiming at the robust parameter design problem under model uncertainty, on the basis of Bayesian model averaging method, Bayesian Model Averaging based on Effect Principle （BMA-EP） robust design methodology was proposed by taking the factor effect principle into account. With prior information and Bayesian law, the main effects＇ posterior probabilities and a general BMA model were obtained. According to these posterior probabilities, all main effects＇ prior probabilities were updated by using effect hierarchy principle and effect heredity principle step by step, and the model＇s posterior probabilities were identified. With these posterior probabilities as weights, weighted average for all models was carried out to obtain the robust model which had better predicted performance and experiment design principle. The effectiveness of proposed method was verified through a practical industrial ex- ample combined with a simulation example. The results revealed that the method not only improved the performance of model prediction, but also increased the reliability of optimal parameter settings.
出处
《计算机集成制造系统》
EI
CSCD
北大核心
2013年第8期1967-1974,共8页
Computer Integrated Manufacturing Systems
基金
国家自然科学基金资助项目(70931002
71211140350
71371099)
南京理工大学自主科研资助项目(AE88831)~~
