Robust recursive identification of multidimensional linear regression models
作者:
LEI GUO,
LIGE XIA,
JOHNB. MOORE,
期刊:
International Journal of Control
(Taylor Available online 1988)
卷期:
Volume 48,
issue 3
页码: 961-979
ISSN:0020-7179
年代: 1988
DOI:10.1080/00207178808906229
出版商: Taylor & Francis Group
数据来源: Taylor
摘要:
Stochastic adaptive estimation and control algorithms involving recursive prediction estimates have guaranteed convergence rates when the noise is not ‘too’ coloured, as when a positive-real condition on the noise mode is satisfied. Moreover, the whiter the noise environment the more robust are the algorithms. This paper shows that for linear regression signal models, the suitable introduction of while noise into the estimation algorithm can make it more robust without compromising on convergence rates. Indeed, there are guaranteed attractive convergence rates independent of the process noise colour. No positive-real condition is imposed on the noise model.
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