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首页> 外文期刊>Mechanical systems and signal processing >On the identification of hysteretic systems. Part II: Bayesian sensitivity analysis and parameter confidence
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On the identification of hysteretic systems. Part II: Bayesian sensitivity analysis and parameter confidence

机译：关于滞后系统的识别。第二部分：贝叶斯敏感性分析和参数置信度

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This paper forms the second in a short sequence considering the system identification problem for hysteretic systems. The basic model for parameter estimation is assumed to be the Bouc-Wen model as this has proved particularly versatile in the past. Previous work on the Bouc-Wen system has shown that the system response is more sensitive to some parameters than others and that the errors in the associated parameter estimates vary as a consequence. The first objective of the current paper is to demonstrate the use of a principled Bayesian approach to parameter sensitivity analysis for the Bouc-Wen system. The approach is based on Gaussian process emulation and is encoded in the software package GEM-SA. The paper considers a five-parameter Bouc-Wen model, and the sensitivity analysis is based on data generated by computer simulation of a single-degree-of-freedom system. The second major objective of the paper is also concerned with uncertainty analysis and considers the problem of obtaining estimates of parameter confidence intervals from optimisation-based system identification schemes. Two different estimators of the parameter covariance matrix are demonstrated and the results are compared with those from an independent MCMC (Markov Chain Monte Carlo) identification method.
机译：考虑到滞后系统的系统识别问题，本文以简短的顺序构成了第二篇。假定参数估计的基本模型是Bouc-Wen模型，因为在过去已证明它特别通用。 Bouc-Wen系统的先前工作表明，系统响应对某些参数的敏感性比对其他参数的敏感性更高，因此相关参数估计中的误差也会发生变化。本文的第一个目标是证明Bouc-Wen系统在参数敏感性分析中使用有原则的贝叶斯方法。该方法基于高斯过程仿真，并编码在软件包GEM-SA中。本文考虑了一个五参数的Bouc-Wen模型，敏感性分析是基于单自由度系统的计算机仿真生成的数据。本文的第二个主要目标还涉及不确定性分析，并考虑了从基于优化的系统识别方案中获取参数置信区间估计值的问题。演示了参数协方差矩阵的两种不同估计量，并将结果与​​独立MCMC（马尔可夫链蒙特卡洛）识别方法的结果进行了比较。

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