An adaptive algorithm for identification of linear systems from noisy measurements

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Abstract

This paper proposes a new approach to adaptive identification of linear noisy input-output systems. This adaptive parameter estimation algorithm is developed based on the bias-correction principle, thus having the expected consistent convergence. Moreover, this algorithm does not involve prefiltering of noisy measurements or parameter extraction during the adaptation. Computer simulations are used to demonstrate its good performance in comparison with other on-line identification methods.

Original languageEnglish
Pages (from-to)474-488
Number of pages15
JournalDynamics of Continuous, Discrete and Impulsive Systems, Series B: Applications and Algorithms
Volume6
Issue number4
Publication statusPublished - Dec 1999

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