Combined regressor methods and adaptive IIR filtering

TitleCombined regressor methods and adaptive IIR filtering
Publication TypeJournal Article
Year of Publication2004
AuthorsAvessta, N., and T. Aboulnasr
JournalCircuits and Systems I: Regular Paper s, IEEE Transactions on
Volume51
Pagination2222 - 2234
Date Publishednov.
ISSN1549-8328
Keywordsadaptive filters, adaptive infinite-impulse response filtering, combined regressor methods, digital filters, EEOE, equation error formulation, IIR filters, observation noise, output error, regression analysis
Abstract

An open issue in adaptive infinite-impulse response (IIR) filtering is that of convergence to a global minimum in the presence of observation noise when the system is insufficiently modeled . It is well known , that algorithms based on equation error (EE) formulation contain a single minimum that may be biased whereas, algorithms based on output error (OE) ensure the existence of an unbiased global minimum in presence of local minima. Recently, there have been several attempts to combine these formulations in order to ensure the existence and uniqueness of an unbiased minimum. Works presented here, EEOE and modified EEOE (MEEOE), are such attempts in the context of system identification. We will show, analytically and through simulations, the convergence properties of the MEEOE approach, in the context of system identification.

URLhttp://dx.doi.org/10.1109/TCSI.2004.836836
DOI10.1109/TCSI.2004.836836

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