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结构系统识别不确定性分析的Bayes方法及其进展

颜王吉 曹诗泽 任伟新

颜王吉, 曹诗泽, 任伟新. 结构系统识别不确定性分析的Bayes方法及其进展[J]. 应用数学和力学, 2017, 38(1): 44-59. doi: 10.21656/1000-0887.370571
引用本文: 颜王吉, 曹诗泽, 任伟新. 结构系统识别不确定性分析的Bayes方法及其进展[J]. 应用数学和力学, 2017, 38(1): 44-59. doi: 10.21656/1000-0887.370571
YAN Wang-ji, CAO Shi-ze, REN Wei-xin.. Uncertainty Quantification for System Identification Utilizing the Bayesian Theory and Its Recent Advances[J]. Applied Mathematics and Mechanics, 2017, 38(1): 44-59. doi: 10.21656/1000-0887.370571
Citation: YAN Wang-ji, CAO Shi-ze, REN Wei-xin.. Uncertainty Quantification for System Identification Utilizing the Bayesian Theory and Its Recent Advances[J]. Applied Mathematics and Mechanics, 2017, 38(1): 44-59. doi: 10.21656/1000-0887.370571

结构系统识别不确定性分析的Bayes方法及其进展

doi: 10.21656/1000-0887.370571
基金项目: 国家自然科学基金(51408176;51278163);国家重点研发计划(2016YFE0113400)
详细信息
    作者简介:

    颜王吉(1985—),男,研究员,博士,硕士生导师(E-mail: civilyanwj@gmail.com);任伟新(1960—),男,教授,博士,博士生导师(通讯作者. E-mail: renwx@hfut.edu.cn).

  • 中图分类号: O32

Uncertainty Quantification for System Identification Utilizing the Bayesian Theory and Its Recent Advances

Funds: The National Natural Science Foundation of China(51408176;51278163); The National Key Research and Development Project of China(2016YFE0113400)
  • 摘要: 受测试误差、建模误差、数值离散化以及环境变异等因素的影响,结构系统识别过程不可避免地存在不确定性,因此有必要引入概率统计方法来提高其鲁棒性,为工程结构安全监测提供更为可靠的结果.近年来,Bayes(贝叶斯)方法因为其诸多优势在系统识别领域受到了广泛关注.该文梳理了Bayes系统识别的历史脉络和研究进展.从Bayes系统识别的理论框架出发,分析了量化系统识别不确定性两类方法的适用条件与局限性.此外,文章综述了Bayes方法在模态参数识别、有限元模型修正以及结构损伤识别方面进行不确定性分析的理论、实现及其应用.最后对基于Bayes方法进行系统识别研究的发展趋势做出了展望.
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  • 收稿日期:  2016-10-11
  • 修回日期:  2016-12-10
  • 刊出日期:  2017-01-15

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