On the identification of model error through observations of time-varying parameters

P. L. Green, E. Chodora, S. Atamturktur

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

When performing system identification, it can be possible to realise a deficient model (i.e. one that will make low fidelity predictions) that is able to closely represent a set of training data. For example, the parameters of linear dynamical models can often be tuned to realise a close match to training data that was generated from a system with strong nonlinearities. Despite this close match to available data, these same models may make very poor-quality predictions when shifted even slightly from the 'validation domain' (which could, for example, be a specific time window). In this paper we investigate the hypothesis that, by treating our model's parameters as being time-varying, we can identify key weaknesses in a model that would have been difficult to establish using other identification methods that do not consider the potentially time-varying nature of the model's parameters. Specifically, we use an Extended Kalman Filter to 'track' the parameters of a dynamical system, as a time history of training data is analysed. We then illustrate that this approach can reveal important information about the potential deficiencies of a model.

Original languageEnglish (US)
Title of host publicationProceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics
EditorsD. Moens, W. Desmet, B. Pluymers, W. Rottiers
PublisherKU Leuven - Departement Werktuigkunde
Pages2759-2773
Number of pages15
ISBN (Electronic)9789073802995
StatePublished - Jan 1 2018
Event28th International Conference on Noise and Vibration Engineering, ISMA 2018 and 7th International Conference on Uncertainty in Structural Dynamics, USD 2018 - Leuven, Belgium
Duration: Sep 17 2018Sep 19 2018

Publication series

NameProceedings of ISMA 2018 - International Conference on Noise and Vibration Engineering and USD 2018 - International Conference on Uncertainty in Structural Dynamics

Other

Other28th International Conference on Noise and Vibration Engineering, ISMA 2018 and 7th International Conference on Uncertainty in Structural Dynamics, USD 2018
Country/TerritoryBelgium
CityLeuven
Period9/17/189/19/18

All Science Journal Classification (ASJC) codes

  • Mechanical Engineering
  • Mechanics of Materials
  • Acoustics and Ultrasonics

Fingerprint

Dive into the research topics of 'On the identification of model error through observations of time-varying parameters'. Together they form a unique fingerprint.

Cite this