Model selection and parameter estimation in structural dynamics using approximate Bayesian computation
Model selection and parameter estimation in structural dynamics using approximate Bayesian computation
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DOI：
10.1016/j.ymssp.2017.06.017
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发表时间：
2018-01
影响因子：
8.4
通讯作者：
A. B. Abdessalem;N. Dervilis;D. Wagg;K. Worden
中科院分区：
文献类型：
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作者：
A. B. Abdessalem;N. Dervilis;D. Wagg;K. Worden
本文将介绍近似贝叶斯计算（ABC）算法在结构动力学模型选择和参数估计中的应用。ABC是一种无似然方法，通常用于似然函数难以处理或无法以封闭形式接近时。为了规避似然函数的评估，来自前向模型的模拟是ABC算法的核心。该算法提供了使用不同的指标和汇总统计数据代表的数据进行贝叶斯推理的可能性。该算法在结构动力学中的有效性通过三个不同的非线性系统识别的说明性例子：三次和五次模型，Bouc-Wen模型和Duffing振子。所得到的结果表明，ABC是一个很有前途的替代处理模型选择和参数估计问题，特别是对于具有复杂行为的系统。
This paper will introduce the use of the approximate Bayesian computation (ABC) algorithm for model selection and parameter estimation in structural dynamics. ABC is a likelihood-free method typically used when the likelihood function is either intractable or cannot be approached in a closed form. To circumvent the evaluation of the likelihood function, simulation from a forward model is at the core of the ABC algorithm. The algorithm offers the possibility to use different metrics and summary statistics representative of the data to carry out Bayesian inference. The efficacy of the algorithm in structural dynamics is demonstrated through three different illustrative examples of nonlinear system identification: cubic and cubic-quintic models, the Bouc-Wen model and the Duffing oscillator. The obtained results suggest that ABC is a promising alternative to deal with model selection and parameter estimation issues, specifically for systems with complex behaviours.