Efficient Parameterization of Nonlinear System Models : a Comment on Noel and Schoukens

Young, Peter C (2020) Efficient Parameterization of Nonlinear System Models : a Comment on Noel and Schoukens. International Journal of Control, 93 (7). pp. 1591-1595. ISSN 0020-7179

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Nöel, J. P., & Schoukens, J. [2018. Grey-box state-space identification of nonlinear mechanical vibrations. International Journal of Control, 91, 1–22] discuss a methodology for the discrete-time state-space identification of nonlinear systems and apply this to experimental data from the well known Silverbox nonlinear circuit, producing a model characterised by 13 parameters. This model explains the data very well but the parameter estimates are not well defined in the optimisation results, with the very large confidence bounds suggesting that the model is over-parameterised. This comment shows that this is indeed the case and that the data can be explained equally well by an alternative continuous-time, State-Dependent Parameter (SDP) transfer function model with only 6 parameters, the estimates of which are well defined with very tight confidence bounds. The comment also raises questions about how the model form for nonlinear systems such as the Silverbox should be identified and suggests that the Data-Based Mechanistic (DBM) approach to modelling has some advantages in this regard.

Item Type:
Journal Article
Journal or Publication Title:
International Journal of Control
Additional Information:
This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Control on 13 Sep 2018, available online:  https://www.tandfonline.com/doi/abs/10.1080/00207179.2018.1521008
Uncontrolled Keywords:
?? system identificationsilverbox systemnonlinear modellingcontinuous-time modelefficient parameterisationcontrol and systems engineeringcomputer science applications ??
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Deposited On:
10 Sep 2018 13:54
Last Modified:
18 Feb 2024 00:51