On the identification of non-stationary linear processes

Identification algorithms for non-stationary linear processes are reviewed. In control, signal processing and many other areas of applications, to track the time varying dynamics of a system is a fundamental problem. The methods considered are the recursive least squares, the Kalman filter and the s...

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Bibliographic Details
Published in:International journal of systems science 2000-03, Vol.31 (3), p.273-286
Main Authors: Bouzeghoub, M. C., Ellacott, S. W., Easdown, A.
Format: Article
Language:English
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Summary:Identification algorithms for non-stationary linear processes are reviewed. In control, signal processing and many other areas of applications, to track the time varying dynamics of a system is a fundamental problem. The methods considered are the recursive least squares, the Kalman filter and the stochastic approaches. Special attention is paid to the study of how different design parameters affect these algorithms. Simulation examples are shown to demonstrate the character of the trade-off between the tracking ability and noise rejection. Also, the comparison provides considerable insight into the application of these methods to the on-line identification.
ISSN:0020-7721
1464-5319
DOI:10.1080/002077200291127