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Bayesian System Identification of MDOF Nonlinear Systems Using Highly Informative Training Data

  • P. L. Green
Conference paper
Part of the Conference Proceedings of the Society for Experimental Mechanics Series book series (CPSEMS)

Abstract

The aim of this paper is to utilise the concept of “highly informative training data” such that, using Markov chain Monte Carlo (MCMC) methods, one can apply Bayesian system identification to multi-degree-of-freedom nonlinear systems with relatively little computational cost. Specifically, the Shannon entropy is used as a measure of information content such that, by analysing the information content of the posterior parameter distribution, one is able to select and utilise a relatively small but highly informative set of training data (thus reducing the cost of running MCMC).

Keywords

System identification Bayesian inference Markov chain Monte Carlo Shannon entropy Nonlinear dynamics 

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Copyright information

© The Society for Experimental Mechanics, Inc. 2014

Authors and Affiliations

  1. 1.Department of Mechanical EngineeringUniversity of SheffieldSheffieldUK

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