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Some asymptotic results in recursive identification using Laguerre models
Linköping University.
KTH, Superseded Departments, Signals, Sensors and Systems.ORCID iD: 0000-0002-1927-1690
1991 (English)In: International Journal of Adaptive Control and Signal Processing, ISSN 08906327 (ISSN), Vol. 5, no 5, 313-333 p.Article in journal (Refereed) Published
Abstract [en]

This paper deals with recursive identification of time-varying systems using Laguerre models. Laguerre models generalize finite impulse response (FIR) models by using a priori information about the dominating time constants of the system to be identified. Three recursive algorithms are considered: the stochastic gradient algorithm, the recursive least squares algorithm and a Kalman-filter-like recursive identification algorithm. Simple and explicit expressions for the model quality are derived under the assumptions that the system varies slowly, that the model is updated slowly and that the model order is high. The derived expressions show how the use of Laguerre models affects the model quality with respect to tracking capability and disturbance rejection.

Place, publisher, year, edition, pages
1991. Vol. 5, no 5, 313-333 p.
Keyword [en]
Computer Programming--Algorithms, Mathematical Techniques, Signal Filtering and Prediction--Kalman Filtering, Systems Science and Cybernetics--Identification, FIR models, Laguerre models, Least square algorithms, Recursive identification, Control Systems, Time Varying
National Category
Control Engineering
URN: urn:nbn:se:kth:diva-55436OAI: diva2:471585
Correspondence Address: Gunnarsson, Svante; Linkoeping Univ, Linkoeping, Sweden NR 20140805Available from: 2012-01-02 Created: 2012-01-02 Last updated: 2013-09-05Bibliographically approved

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ReferencesLink to record
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