On robustness in system identification
type: Item
instance of: scholarly article
title: On robustness in system identification
zbMATH Open document ID: 0933.93032
DOI: 10.1016/S0005-1098(98)00223-4
published in: Automatica
publication date: 2000-04-04T00:00:00Z
review text: This paper studies robustness issues in system identification. A framework for robust convergence analysis is given. It uses a set of results on robust convergence under average \(l_p\) norms, Orlicz norms and cross-correlation-type noise semi-norms. The new results are used to discuss and to study robustness issues in identification of linear time-invariant models when the true plant is not exactly linear. The results show that the effects of unmodelled dynamics are typically more serious than those of random noise. A divergence result is also given and additive error analysis is presented.
reviewed by: Yves Cherruault
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93B35
zbMATH DE Number: 1325761
zbMATH Keywords: noise models
zbMATH Keywords: robustness
zbMATH Keywords: system identification
zbMATH Keywords: norms
Wikidata QID: Q56319459
author: P. M. Mäkilä
author: Jonathan R. Partington
MaRDI profile type: Publication
full work available at URL: s0005-1098(98)00223-4
OpenAlex ID: W1977310743
description: scientific article; zbMATH DE number 1325761