NDLI: Rank and order conditions for identifiability analysis of linear systems with standard parameterized control and observation matrices

Content Provider	IEEE Xplore Digital Library
Author	Avdeenko, T.V.
Copyright Year	2015
Description	Author affiliation: Bus. Inf. Dept., Novosibirsk State Tech. Univ., Novosibirsk, Russia (Avdeenko, T.V.)
Abstract	In present paper the approach to testing generically both local and global identifiability of linear large scale systems based on rank and order conditions is further developed. This method was initially proposed for the most general case of parametrization when estimated parameters are the elements of system matrices with linear constraints on them. Here we adapt the approach for the more special case when we have standard form of control and observation matrices with only one nonzero element, which can be estimated, in each row (column) of observation (control) matrix. Despite this limitation such model structures are often used in practice, for example, in compartmental modeling. Taking into account special structure of this class of model structures we succeed in decreasing the dimensions of the identifiability matrices in the rank conditions that is significant for the symbolic computations of generic rank. Along this way we have obtained the so called order conditions - relations between the number of constraints and the dimensions of system matrices - that could be useful at the first stages of modeling allowing discard the whole bunch of non-identifiable model structures. An example is given to illustrate the approach.
Starting Page	1321
Ending Page	1326
File Size	239314
Page Count	6
File Format	PDF
e-ISBN	9781479977871
DOI	10.1109/CCA.2015.7320795
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-21
Publisher Place	Australia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Computational modeling Mathematical model Numerical models Standards Analytical models Sufficient conditions Adaptation models
Content Type	Text
Resource Type	Article

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