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首页> 外文期刊>Computer-Aided Civil and Infrastructure Engineering >Full Gibbs Sampling Procedure for Bayesian System Identification Incorporating Sparse Bayesian Learning with Automatic Relevance Determination
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Full Gibbs Sampling Procedure for Bayesian System Identification Incorporating Sparse Bayesian Learning with Automatic Relevance Determination

机译：贝叶斯系统识别的完整Gibbs采样过程，将稀疏贝叶斯学习与自动相关性确定相结合

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Bayesian system identification has attracted substantial interest in recent years for inferring structural models and quantifying their uncertainties based on measured dynamic response in a structure. The relative plausibility of each structural model in a specified model class is quantified by its posterior probability from Bayes' Theorem. The relative plausibility of each model class within a set of candidate model classes for the structure can also be assessed via Bayes' Theorem. Computation of this posterior probability over all candidate model classes automatically applies a quantitative Ockham's razor that trades off a data-fit measure with an information-theoretic measure of model complexity, which penalizes model classes that "over-fit" the data. In this article, we first present a general Bayesian system identification framework and point out that combining it with sparse Bayesian learning (SBL) is an effective strategy to implement the Bayesian Ockham razor. Then we review our recent progress in exploring SBL with the automatic relevance determination likelihood concept to detect and quantify spatially sparse substructure stiffness reductions. To characterize the full posterior uncertainty for this problem, an improved Gibbs sampling procedure for SBL is then developed. Finally, illustrative results are provided to compare the performance and validate the capability of the presented SBL algorithms for structural system identification.
机译：近年来，贝叶斯系统识别已经引起了人们的广泛兴趣，他们可以根据结构中的动态响应来推断结构模型并量化其不确定性。每个结构模型在指定模型类别中的相对合理性通过贝叶斯定理的后验概率来量化。也可以通过贝叶斯定理评估该结构的一组候选模型类中每个模型类的相对合理性。在所有候选模型类别上计算此后验概率会自动应用定量的Ockham剃刀，该剃刀在数据拟合度量与模型复杂度的信息理论度量之间进行权衡，这会惩罚“过度拟合”数据的模型类别。在本文中，我们首先提出一个通用的贝叶斯系统识别框架，并指出将其与稀疏贝叶斯学习（SBL）结合使用是实施贝叶斯Ockham剃刀的有效策略。然后，我们回顾了我们在利用自动相关性确定可能性概念探索SBL以检测和量化空间稀疏子结构刚度降低方面的最新进展。为了表征该问题的全部后验不确定性，然后针对SBL开发了改进的Gibbs采样程序。最后，提供了说明性结果以比较性能并验证所提出的SBL算法用于结构系统识别的能力。

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    Huang Yong; Beck James L.;

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    Harbin Inst Technol, Sch Civil Engn, Minist Educ, Key Lab Struct Dynam Behav & Control, Harbin, Heilongjiang, Peoples R China;

    CALTECH, Div Engn & Appl Sci, Pasadena, CA 91125 USA;

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