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

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Efficient Parameterization of Nonlinear System Models : a Comment on Noel and Schoukens. / Young, Peter C.

In: International Journal of Control, Vol. 93, No. 7, 01.07.2020, p. 1591-1595.

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Young, Peter C. / Efficient Parameterization of Nonlinear System Models : a Comment on Noel and Schoukens. In: International Journal of Control. 2020 ; Vol. 93, No. 7. pp. 1591-1595.

Bibtex

@article{7f942bffa66b4e768e1e54cc1e5cac9e,
title = "Efficient Parameterization of Nonlinear System Models: a Comment on Noel and Schoukens",
abstract = "N{\"o}el, 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.",
keywords = "System identification, silverbox system, nonlinear modelling, continuous-time model, efficient parameterisation",
author = "Young, {Peter C}",
note = "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",
year = "2020",
month = jul,
day = "1",
doi = "10.1080/00207179.2018.1521008",
language = "English",
volume = "93",
pages = "1591--1595",
journal = "International Journal of Control",
issn = "0020-7179",
publisher = "Taylor and Francis Ltd.",
number = "7",

}

RIS

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 -