Fast Bayesian identification of a class of elastic weakly nonlinear systems using backbone curves
Fast Bayesian identification of a class of elastic weakly nonlinear systems using backbone curves
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DOI：
10.1016/j.jsv.2015.09.007
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发表时间：
2016-01-06
影响因子：
4.7
通讯作者：
Neild, S. A.
中科院分区：
文献类型：
--
作者：
Hill, T. L.;Green, P. L.;Neild, S. A.
本文介绍了一种基于概率贝叶斯框架的非线性结构参数识别方法，该方法采用马尔可夫链蒙特卡罗算法。这种方法使用分析模型来描述结构在频率振幅域中的无强迫、无阻尼动态响应，称为主干曲线。描述这些骨干曲线的分析模型，然后拟合到测量的响应，发现使用共振诱饵方法。为了研究所提出的识别方法，对一个非线性两实例结构进行了数值模拟，得到了描述骨架曲线的解析表达式，并结合模拟实验得到的骨架曲线数据，对系统参数进行了估计。它示出，使用这些计算上便宜的解析表达式允许一个非常有效的方法建模的动态行为，提供了一个识别过程，是快速和准确的。此外，对于示例结构，示出了估计的参数可以用于准确地预测远离所提供的主干曲线数据的动态行为的存在;特别是预测isola的存在。(C)2015年，作者。由Elsevier Ltd.发布。这是CC BY许可证下的开放获取文章（http：//creativecommands.org/licenses/by/4.0）。
This paper introduces a method for the identification of the parameters of nonlinear structures using a probabilistic Bayesian framework, employing a Markov chain Monte Carlo algorithm. This approach uses analytical models to describe the unforced, undamped dynamic responses of structures in the frequency amplitude domain, known as the backbone curves. The analytical models describing these backbone curves are then fitted to measured responses, found using the resonant decoy method. To investigate the proposed identification method, a nonlinear two example structure is simulated numerically and analytical expressions describing the backbone curves are found. These expressions are then used, in conjunction with the backbone curve data found through simulated experiment, to estimate the system parameters. It is shown that the use of these computationally cheap analytical expressions allows for an extremely efficient method for modelling the dynamic behaviour, providing an identification procedure that is both fast and accurate. Furthermore, for the example structure, it is shown that the estimated parameters may be used to accurately predict the existence of dynamic behaviours that are well-away from the backbone curve data provided; specifically the existence of an isola is predicted. (C) 2015 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommuns.org/licenses/by/4.0).