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Stabilized Adaptive Forgetting in Recursive Least Squares Estimators

Author(s):

J. Milek, F. Kraus
Conference/Journal:

vol. AUT93-05
Abstract:

Parameter estimators for time-varying processes are proposed as LS-based recursive algorithms with on-line tuned forgetting. The algorithms utilize a wide class of time-varying stabilized forgetting functions and guarantee very good convergence, eliminate blow up of the covariance matrix, have good numerical properties and possess a high degree of adaptability. It is shown that the algorithms can be on--line adapted for possibly time-varying environments, using simple ad hoc mechanisms.

Year:

1993
Type of Publication:

(04)Technical Report
Supervisor:



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% Autogenerated BibTeX entry
@TechReport { MilKra:1993:IFA_1447,
    author={J. Milek and F. Kraus},
    title={{Stabilized Adaptive Forgetting in Recursive Least Squares
	  Estimators}},
    institution={},
    year={1993},
    number={},
    address={},
    url={http://control.ee.ethz.ch/index.cgi?page=publications;action=details;id=1447}
}
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