Bounded-error parameter estimation: Noise models and recursive algorithms
type: Item
instance of: scholarly article
title: Bounded-error parameter estimation: Noise models and recursive algorithms
zbMATH Open document ID: 0878.93018
DOI: 10.1016/0005-1098(96)00040-4
author: Krishna M. Nagpal
author: Er-wei Bai
author: Roberto Tempo
published in: Automatica
publication date: 1996-10-01T00:00:00Z
review text: The paper deals with basic issues associated with the set membership approach to bounded noise parameter estimation problems: estimates consistency, structure of the experimental data and the a priori information about the noise. A relationship between various noise models (noise upper bounds) and the `size' of the set of parameter estimates being consistent with the data and the a priori information is established. Next, the efficient recursive estimation algorithms, producing parameter estimates that converge to the membership set, are presented for most commonly considered noise models. Also a recursive membership set parameter estimation method that is worst-case optimal is proposed.
Mathematics Subject Classification ID: 93B30
Mathematics Subject Classification ID: 93B40
Mathematics Subject Classification ID: 93A30
zbMATH DE Number: 930678
zbMATH Keywords: set membership
zbMATH Keywords: bounded noise parameter estimation
zbMATH Keywords: consistency
zbMATH Keywords: recursive estimation algorithms
reviewed by: Zygmunt Hasiewicz
MaRDI profile type: Publication
full work available at URL: 0005-1098(96)00040-4
OpenAlex ID: W2126296227
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Wikidata QID: Q126851467
description: scientific article; zbMATH DE number 930678