System identification using high-order models, revisited
1989 (English)In: Proceedings of the 28th IEEE Conference on Decision and Control. Part 1 (of 3), 1989, Vol. 1, no Piscataway, NJ, United States, 634-639 p.Conference paper (Refereed)
The traditional approach of expanding transfer functions and noise models in the delay operator to obtain predictor models linear in the parameters leads to approximations of very high order in the case of rapid sampling and/or large dispersion in time constants. By using a priori information about the time constants of the system, more appropriate expansions, closely related to Laguerre networks, are introduced and analyzed. It is shown that these expansions need much lower orders to obtain reasonable approximations and improve the numerical properties of the estimation algorithm. Consistency (error bounds), persistence of excitation conditions, and asymptotic statistical properties are investigated.
Place, publisher, year, edition, pages
1989. Vol. 1, no Piscataway, NJ, United States, 634-639 p.
Computer Programming--Algorithms, Mathematical Techniques--Transfer Functions, Statistical Methods, Laguerre Networks, Time Constants, Control systems
IdentifiersURN: urn:nbn:se:kth:diva-55451DOI: 10.1109/CDC.1989.70196OAI: oai:DiVA.org:kth-55451DiVA: diva2:471570
28th IEEE Conference on Decision and Control. Tampa, FL, USA. 13 December 1989 - 15 December 1989
QC 20120104. Sponsors: IEEE Control Systems Soc, New York, NY, USA2012-01-022012-01-022013-09-05Bibliographically approved