Version 1
: Received: 10 June 2023 / Approved: 12 June 2023 / Online: 12 June 2023 (04:42:25 CEST)
How to cite:
Karabutov, N. On Structural Identifiability of System with Nonsymmetric Nonlinearities. Preprints2023, 2023060757. https://doi.org/10.20944/preprints202306.0757.v1
Karabutov, N. On Structural Identifiability of System with Nonsymmetric Nonlinearities. Preprints 2023, 2023060757. https://doi.org/10.20944/preprints202306.0757.v1
Karabutov, N. On Structural Identifiability of System with Nonsymmetric Nonlinearities. Preprints2023, 2023060757. https://doi.org/10.20944/preprints202306.0757.v1
APA Style
Karabutov, N. (2023). On Structural Identifiability of System with Nonsymmetric Nonlinearities. Preprints. https://doi.org/10.20944/preprints202306.0757.v1
Chicago/Turabian Style
Karabutov, N. 2023 "On Structural Identifiability of System with Nonsymmetric Nonlinearities" Preprints. https://doi.org/10.20944/preprints202306.0757.v1
Abstract
The complexity of objects and control systems increases the requirements for mathematical models. The structural identifiability (SI) assessment of nonlinear systems is one of the identification problems. Until now, this problem solves by parametric methods using various approximation methods. This approach is not always effective under uncertainty. We apply an approach to SI estimation based on the analysis of virtual structures. The requirements form for the system input based on the expansion of the excitation constancy property and S-synchronizability. The approach generalization to structural identifiability used in the analysis of systems with symmetric nonlinearity is given. homothety and identifiability conditions for systems with non-symmetric nonlinearities (SNN) obtained. The detectability and recoverability proofed for virtual frameworks (VF) guaranteed the SI estimation under uncertainty. The conditions under which the non-symmetric nonlinearity is hypothetical symmetric nonlinearity obtained. SI estimation examples considered for closed nonlinear systems under uncertainty.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
