Bayesian system identification of dynamical systems using highly informative training data
Bayesian system identification of dynamical systems using highly informative training data
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DOI：
10.1016/j.ymssp.2014.10.003
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发表时间：
2015-05-01
影响因子：
8.4
通讯作者：
Worden, K.
中科院分区：
文献类型：
--
作者：
Green, P. L.;Cross, E. J.;Worden, K.
本文涉及使用实验获得的训练数据对结构动力系统进行贝叶斯系统识别。它的动机是，人们必须从大量训练数据中选择一个子集来推断概率模型。为此，使用信息论中的概念，导出表达式，使人们能够近似一组训练数据对参数不确定性以及候选模型结构的合理性的影响。然后，通过使用基于物理的模型和仿真器模型对多个动力系统进行系统识别，证明了这一概念的有用性。其结果是一个严格的科学框架，可用于从大量训练数据中选择“信息丰富”的子集。 (C) 2014 年作者。由爱思唯尔有限公司出版
This paper is concerned with the Bayesian system identification of structural dynamical systems using experimentally obtained training data. It is motivated by situations where, from a large quantity of training data, one must select a subset to infer probabilistic models. To that end, using concepts from information theory, expressions are derived which allow one to approximate the effect that a set of training data will have on parameter uncertainty as well as the plausibility of candidate model structures. The usefulness of this concept is then demonstrated through the system identification of several dynamical systems using both physics-based and emulator models. The result is a rigorous scientific framework which can be used to select 'highly informative' subsets from large quantities of training data. (C) 2014 The Authors. Published by Elsevier Ltd.