Extending a sensitivity based algorithm to detect local structural identifiability

L.G. van Willigenburg*, J.D. Stigter, J. Molenaar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademicpeer-review

2 Citations (Scopus)

Abstract

Output sensitivities to parameters underly a highly efficient sensitivity based algorithm to compute local structural identifiability of possibly large-scale nonlinear dynamic systems. By means of simple examples, this paper explores exceptional cases where this algorithm fails. That is, if one applies the common definition of local structural identifiability. As also shown in this paper, when applying a closely related definition of identifiability, based on sensitivities, the sensitivity based algorithm always provides the correct answer. The subtle difference between these two definitions, that seems to have been overlooked in the literature, is further explored and explained in this paper. For the common definition of local structural identifiability, an extension of the sensitivity based algorithm is presented that approximately doubles the computation time. This extension is shown to work along non-singular trajectories.

Original languageEnglish
Title of host publication10th Vienna International Conference on Mathematical Modelling MATHMOD 2022
EditorsA. Kugi, A. Körner, W. Kemmetmüller, A. Deutschmann-Olek, F. Breitenecker, I. Troch
PublisherElsevier
Pages343-348
Number of pages6
DOIs
Publication statusPublished - 23 Sept 2022
Event10th Vienna International Conference on Mathematical Modelling, MATHMOD 2022 - Vienna, Austria
Duration: 27 Jul 202229 Jul 2022

Publication series

NameIFAC-PapersOnline
PublisherElsevier
Number20
Volume55
ISSN (Print)2405-8963

Conference/symposium

Conference/symposium10th Vienna International Conference on Mathematical Modelling, MATHMOD 2022
Country/TerritoryAustria
CityVienna
Period27/07/2229/07/22

Keywords

  • Large-scale nonlinear dynamic systems
  • Local sensitivity identifiability
  • Local structural identifiability
  • Sensitivity based algorithm
  • Sensitivity rank condition (SERC)

Fingerprint

Dive into the research topics of 'Extending a sensitivity based algorithm to detect local structural identifiability'. Together they form a unique fingerprint.

Cite this