摘要翻译：
本文比较了最大似然法和贝叶斯法在有限元模型修正中的应用。最大似然法是用遗传算法实现的，贝叶斯法是用马尔可夫链蒙特卡罗实现的。这些方法在简支梁和非对称H形结构上进行了试验。结果表明，贝叶斯方法给出的改进的有限元模型比使用最大似然方法得到的改进的有限元模型预测的模态特性更准确。此外，发现这两种方法都需要相同水平的计算负荷。
---
英文标题：
《Finite Element Model Updating Using Bayesian Approach》
---
作者：
Tshilidzi Marwala, Lungile Mdlazi and Sibusiso Sibisi
---
最新提交年份：
2007
---
分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
--

---
英文摘要：
  This paper compares the Maximum-likelihood method and Bayesian method for finite element model updating. The Maximum-likelihood method was implemented using genetic algorithm while the Bayesian method was implemented using the Markov Chain Monte Carlo. These methods were tested on a simple beam and an unsymmetrical H-shaped structure. The results show that the Bayesian method gave updated finite element models that predicted more accurate modal properties than the updated finite element models obtained through the use of the Maximum-likelihood method. Furthermore, both these methods were found to require the same levels of computational loads.
---
PDF链接：
https://arxiv.org/pdf/705.2515