Some results and applications about identifiability of non-linear systems

Denis-Vidal, Lilianne; Joly-Blanchard, Ghislaine; Noiret, Céline

doi:10.23919/ecc.1999.7099478

Cited by 4 publications

(2 citation statements)

References 6 publications

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“…This should be formally extended. While the initial conditions were considered arbitrary in this paper, there are cases where such an assumption can be detrimental (see Example 4 of Villaverde and Banga (2017) and Example 2 of Denis-Vidal et al (1999)). These cases need to be carefully handled in the implementation to cover a larger spectrum of models and initial conditions.…”

Section: Concluding Remarks and Perspectivesmentioning

confidence: 99%

Parameter identifiability for nonlinear LPV models

Srinivasarengan

Ragot

Aubrun

et al. 2022

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Linear parameter varying (LPV) models are being increasingly used as a bridge between linear and nonlinear models. From a mathematical point of view, a large class of nonlinear models can be rewritten in LPV or quasi-LPV forms easing their analysis. From a practical point of view, that kind of model can be used for introducing varying model parameters representing, for example, nonconstant characteristics of a component or an equipment degradation. This approach is frequently employed in several model-based system maintenance methods. The identifiability of these parameters is then a key issue for estimating their values based on which a decision can be made. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify the identifiability of unknown parameters for LPV or quasi-LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analyzed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time models and is illustrated through various examples.

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Section: Concluding Remarks and Perspectivesmentioning

confidence: 99%

Parameter identifiability for nonlinear LPV models

Srinivasarengan

Ragot

Aubrun

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Model is not Identifiable 19: end if considered arbitrary in this paper, there are cases where such assumption can be detrimental (See Example 4 in Villaverde and Banga (2017) and Example 2 in Denis-Vidal et al (1999)). These cases need to be carefully handled in the implementation to cover a larger spectrum of models and initial conditions.…”

Section: Concluding Remarks and Perspectivesmentioning

confidence: 99%

An approach to parameter identifiability for a class of nonlinear models represented in LPV form

Srinivasarengan,

Ragot,

Aubrun

et al. 2020

Preprint

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In several model-based system maintenance problems, parameters are used to represent unknown characteristics of a component, equipment degradation, etc. This allows for modelling constant, slow-varying terms. The identifiability of these parameters is an important condition to estimate them. Linear Parameter Varying (LPV) models are being increasingly used in the industries as a bridge between linear and nonlinear models. Techniques exist that can rewrite some nonlinear models in LPV form. However, the problem of identifiability of these models is still at a nascent stage. In this paper, we propose an approach to verify identifiability of unknown parameters for LPV state-space models. It makes use of a parity-space like formulation to eliminate the states of the model. The resulting input-output-parameter equation is analysed to verify the identifiability of the original model or a subset of unknown parameters. This approach provides a framework for both continuous-time and discrete-time models and we illustrate it using examples.

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Identifiability analysis of second-order systems

Scheibe

2003

Nuclear Medicine and Biology

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Some results and applications about identifiability of non-linear systems

Cited by 4 publications

References 6 publications

Parameter identifiability for nonlinear LPV models

Parameter identifiability for nonlinear LPV models

An approach to parameter identifiability for a class of nonlinear models represented in LPV form

Identifiability analysis of second-order systems

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