Bayesian system identification of dynamical systems using highly informative training data



Green, PL, Cross, EJ and Worden, K
(2015) Bayesian system identification of dynamical systems using highly informative training data. Mechanical Systems and Signal Processing, 56-57. pp. 109-122.

[thumbnail of MSSP_2014b.pdf] Text
MSSP_2014b.pdf - Unspecified

Download (751kB)

Abstract

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.

Item Type: Article
Uncontrolled Keywords: Nonlinear system identification, Bayesian inference, Markov chain Monte Carlo, Shannon entropy, Tamar bridge
Depositing User: Symplectic Admin
Date Deposited: 21 Apr 2016 11:01
Last Modified: 19 Jan 2023 07:37
DOI: 10.1016/j.ymssp.2014.10.003
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3000657