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Abstract
In this paper, we consider the problem of noncausal identification of nonstationary, linear stochastic systems, i.e., identification based on prerecorded input/output data. We show how several competing weighted (windowed) least squares parameter smoothers, differing in memory settings, can be combined together to yield a better and more reliable smoothing algorithm. The resulting parallel estimation scheme automatically adjusts its smoothing bandwidth to the unknown, and possibly time-varying, rate of nonstationarity of the identified system. We optimize the window shape for a certain class of parameter variations and we derive computationally attractive recursive smoothing algorithms for such an optimized case.
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- Category:
- Articles
- Type:
- artykuł w czasopiśmie wyróżnionym w JCR
- Published in:
-
AUTOMATICA
no. 47,
pages 2239 - 2244,
ISSN: 0005-1098 - Language:
- English
- Publication year:
- 2011
- Bibliographic description:
- Niedźwiecki M., Gackowski S.: On noncausal weighted least squares identification of nonstationary stochastic systems// AUTOMATICA. -Vol. 47, nr. iss. 10 (2011), s.2239-2244
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.automatica.2011.08.008
- Verified by:
- Gdańsk University of Technology
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