A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models

Carlos Mendez-Blanco; Leyla Ozkan

doi:10.1109/CDC42340.2020.9304489

A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models

Carlos Mendez-Blanco, Leyla Ozkan

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

Abstract

In this paper structural identifiability of state space models, possibly nonlinear in parameters, is assessed by analyzing the controllability of the output sensitivities. Sensitivity analysis provides a mathematical setting to analyze parameter identifiability from a physically intuitive perspective. Both SISO and MIMO cases are treated; in the former case the output controllability matrix rank directly allows to draw conclusions on the model structural identifiability. In the latter case, the analysis requires special attention due to the ordering induced by the vector derivative. The approach is illustrated on a linear compartmental model.

Original language	English
Title of host publication	59th IEEE Conference on Decision and Control (CDC 2020)
Publisher	Institute of Electrical and Electronics Engineers
Pages	294-299
Number of pages	6
ISBN (Electronic)	9781728174471
DOIs	https://doi.org/10.1109/CDC42340.2020.9304489
Publication status	Published - 11 Jan 2021
Event	59th IEEE Conference on Decision and Control, CDC 2020 - Virtual/Online, Virtual, Jeju Island, Korea, Republic of Duration: 14 Dec 2020 → 18 Dec 2020 Conference number: 59 https://cdc2020.ieeecss.org/

Conference

Conference	59th IEEE Conference on Decision and Control, CDC 2020
Abbreviated title	CDC
Country/Territory	Korea, Republic of
City	Virtual, Jeju Island
Period	14/12/20 → 18/12/20
Internet address	https://cdc2020.ieeecss.org/

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10.1109/CDC42340.2020.9304489

Cite this

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title = "A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models",

abstract = "In this paper structural identifiability of state space models, possibly nonlinear in parameters, is assessed by analyzing the controllability of the output sensitivities. Sensitivity analysis provides a mathematical setting to analyze parameter identifiability from a physically intuitive perspective. Both SISO and MIMO cases are treated; in the former case the output controllability matrix rank directly allows to draw conclusions on the model structural identifiability. In the latter case, the analysis requires special attention due to the ordering induced by the vector derivative. The approach is illustrated on a linear compartmental model.",

author = "Carlos Mendez-Blanco and Leyla Ozkan",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 59th IEEE Conference on Decision and Control, CDC 2020, CDC ; Conference date: 14-12-2020 Through 18-12-2020",

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doi = "10.1109/CDC42340.2020.9304489",

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Mendez-Blanco, C & Ozkan, L 2021, A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models. in 59th IEEE Conference on Decision and Control (CDC 2020)., 9304489, Institute of Electrical and Electronics Engineers, pp. 294-299, 59th IEEE Conference on Decision and Control, CDC 2020, Virtual, Jeju Island, Korea, Republic of, 14/12/20. https://doi.org/10.1109/CDC42340.2020.9304489

A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models. / Mendez-Blanco, Carlos; Ozkan, Leyla.
59th IEEE Conference on Decision and Control (CDC 2020). Institute of Electrical and Electronics Engineers, 2021. p. 294-299 9304489.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

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A State and Output Sensitivity Controllability Approach for Structural Identifiability of Linear State Space Models

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