一种基于改进MCMC算法的模型修正方法

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Abstract The standard Markov Chain Monte Carlo (MCMC) algorithm is not easy to converge and the rejection rate is high, which limits its application.The maximum entropy method was introduced into the Bayesian method to estimate the maximum value of the posterior probability density function of the parameters, and then the updating idea of new bird nest in the cuckoo algorithm was integrated into the Metropolis-Hasting (MH) sampling algorithm to obtain an improved MH sampling algorithm.Meanwhile, support vector machine (SVM) was used to establish the surrogate model between the parameters to be updated and the output of the finite element model to improve the computational efficiency of model updating.A linear system with three degrees of freedom (DOFs) and a plane truss model were used to verify the effectiveness of the proposed method.The results show that the Markov chain of the updated sample has better mixing performance, and low stagnation probability, and the relative error of the updated parameters is less than 2%.

Key words： model updating Bayesian estimates support vector machine (SVM) Markov Chain Monte Carlo（MCMC）algorithm cuckoo algorithm

Received: 17 July 2018 Published: 15 February 2020

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	PENG Zhenrui
	ZHENG Jie
	BAI Yu
	YIN Hong

Cite this article:

PENG Zhenrui,ZHENG Jie,BAI Yu, et al. A model updating method based on an improved MCMC algorithm[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(4): 236-245.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I4/236

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