Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing



Green, PL
(2015) Bayesian system identification of a nonlinear dynamical system using a novel variant of Simulated Annealing. Mechanical Systems and Signal Processing, 52-53 (Februa). pp. 133-146.

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Abstract

This work details the Bayesian identification of a nonlinear dynamical system using a novel MCMC algorithm: ‘Data Annealing’. Data Annealing is similar to Simulated Annealing in that it allows the Markov chain to easily clear ‘local traps’ in the target distribution. To achieve this, training data is fed into the likelihood such that its influence over the posterior is introduced gradually - this allows the annealing procedure to be conducted with reduced computational expense. Additionally, Data Annealing uses a proposal distribution which allows it to conduct a local search accompanied by occasional long jumps, reducing the chance that it will become stuck in local traps. Here it is used to identify an experimental nonlinear system. The resulting Markov chains are used to approximate the covariance matrices of the parameters in a set of competing models before the issue of model selection is tackled using the Deviance Information Criterion.

Item Type: Article
Uncontrolled Keywords: Bayesian model updating, Nonlinear system identification, Markov chain, Markov chain Monte Carlo, Monte Carlo, Simulated Annealing, Deviance Information Criterion
Depositing User: Symplectic Admin
Date Deposited: 27 Apr 2016 15:16
Last Modified: 19 Jan 2023 07:37
DOI: 10.1016/j.ymssp.2014.07.010
Related URLs:
URI: https://livrepository.liverpool.ac.uk/id/eprint/3000659