Rights statement: 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
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Final published version
Research output: Contribution to Journal/Magazine › Journal article › peer-review
Research output: Contribution to Journal/Magazine › Journal article › peer-review
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TY - JOUR
T1 - Efficient Parameterization of Nonlinear System Models
T2 - a Comment on Noel and Schoukens
AU - Young, Peter C
N1 - 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
PY - 2020/7/1
Y1 - 2020/7/1
N2 - 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.
AB - 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.
KW - System identification
KW - silverbox system
KW - nonlinear modelling
KW - continuous-time model
KW - efficient parameterisation
U2 - 10.1080/00207179.2018.1521008
DO - 10.1080/00207179.2018.1521008
M3 - Journal article
VL - 93
SP - 1591
EP - 1595
JO - International Journal of Control
JF - International Journal of Control
SN - 0020-7179
IS - 7
ER -