Strong consistency of recursive identification by no use of persistent excitation condition
type: Item
instance of: scholarly article
title: Strong consistency of recursive identification by no use of persistent excitation condition
zbMATH Open document ID: 0638.93071
DOI: 10.1007/BF01539484
author: Lei Guo
author: Hanfu Chen
published in: Acta Mathematicae Applicatae Sinica. English Series
publication date: 1985-01-01T00:00:00Z
review text: For the discrete-time stochastic system without monitoring, the strong consistency of the estimate given by the stochastic gradient algorithm is considered when the persistent excitation condition is possibly not fulfilled. In addition, the convergence rate is given for a specific class of system noises, while in the adaptive tracking case the convergence rate for the parameter estimates as well as the tracking error are obtained when the reference signal is disturbed by a ``dither''.
Mathematics Subject Classification ID: 93E12
Mathematics Subject Classification ID: 93C05
Mathematics Subject Classification ID: 93C55
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 93E10
Mathematics Subject Classification ID: 93E25
zbMATH DE Number: 4041763
zbMATH Keywords: discrete-time stochastic system
zbMATH Keywords: stochastic gradient algorithm
zbMATH Keywords: convergence rate
zbMATH Keywords: parameter estimates
zbMATH Keywords: tracking error
MaRDI profile type: Publication
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full work available at URL: https://doi.org/10.1007/bf01539484
OpenAlex ID: W2079326259
description: scientific article; zbMATH DE number 4041763