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Structural identifiability analysis via symmetries of differential equations

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Yates, James W. T., Evans, Neil D. and Chappell, M. J. (Michael J.) (2009) Structural identifiability analysis via symmetries of differential equations. Automatica, Vol.45 (No.11). pp. 2585-2591. doi:10.1016/j.automatica.2009.07.009 ISSN 0005-1098.

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Official URL: http://dx.doi.org/10.1016/j.automatica.2009.07.009

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Abstract

Results and derivations are presented for the generation of a local Lie algebra that represents the 'symmetries' of a set of coupled differential equations. The subalgebra preserving the observation defined on the model structure is found, thus giving all transformations of the system that preserve its structure. It is shown that this is equivalent to the similarity transformation approach (Evans, Chapman, Chappell, & Godfrey, 2002) for structural identifiability analysis and as such is a method of generating such transformations for this approach. This provides another method for performing structural identifiability analysis on nonlinear state-space models that has the possibility of extension to PDE type models. The analysis is easily automated and performed in MATHEMATICA, and this is demonstrated by application the technique to a number of practical examples from the literature.

Item Type: Journal Article
Subjects: Q Science > QA Mathematics
T Technology > TA Engineering (General). Civil engineering (General)
Divisions: Faculty of Science, Engineering and Medicine > Engineering > Engineering
Library of Congress Subject Headings (LCSH): Differential equations , Parameter estimation, Lie algebras
Journal or Publication Title: Automatica
Publisher: Pergamon
ISSN: 0005-1098
Official Date: November 2009
Dates:
DateEvent
November 2009Published
Volume: Vol.45
Number: No.11
Number of Pages: 7
Page Range: pp. 2585-2591
DOI: 10.1016/j.automatica.2009.07.009
Status: Peer Reviewed
Publication Status: Published
Access rights to Published version: Restricted or Subscription Access
Date of first compliant deposit: 8 December 2015
Date of first compliant Open Access: 8 December 2015

Data sourced from Thomson Reuters' Web of Knowledge

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