Test for local structural identifiability of high-order non-linearly parametrized state space models
type: Item
instance of: scholarly article
title: Test for local structural identifiability of high-order non-linearly parametrized state space models
zbMATH Open document ID: 0919.93025
DOI: 10.1016/0005-1098(96)00021-0
author: Hans G. M. Dötsch
author: Paul Van den Hof
published in: Automatica
publication date: 1999-04-11T00:00:00Z
review text: This paper presents tests for local identifiability of a deterministic single-input single-output linear system based on the sequence of Markov parameters \(\{h(k,\theta), k= 1,2,\dots, 2n\}\) where \(\theta\) is an unknown parameter and \(n\) is an upper bound to the McMillan degree. Part 3 of the paper is called: ``Identifiability analysis based on infinite-time information matrix'' and Part 4 is called ``Identifiability analysis based on finite-time information matrix''. Although the problem stated is deterministic, probabilistic calculations play an important role in this paper. The paper ends with an example. Numerical aspects are stressed.
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93A30
zbMATH DE Number: 930714
zbMATH Keywords: state space models
zbMATH Keywords: gradients
zbMATH Keywords: Lyapunov equation
zbMATH Keywords: local identifiability
zbMATH Keywords: Markov parameters
reviewed by: Tzvetan Semerdjiev
MaRDI profile type: Publication
full work available at URL: 0005-1098(96)00021-0
OpenAlex ID: W2005875220
cites work: Identifiability results on some constrained compartmental systems
cites work: Physically based parameterizations for designing adaptive algorithms
cites work: Parametrizations of linear dynamical systems: Canonical forms and identifiability
cites work: Identifiability of linear and nonlinear dynamical systems
cites work: Q5521815
cites work: Q4724470
cites work: On global identifiability for arbitrary model parametrizations
cites work: Normal-mode identifiability analysis of linear compartmental systems in linear stages
cites work: Q5394909
cites work: Identification in Parametric Models
cites work: Compartment analysis: An inverse problem
cites work: Gradient computation in prediction error identification of linear discrete-time systems
description: scientific article; zbMATH DE number 930714