By Topic

Bounded error identification of time-varying parameters by RLS techniques

Sign In

Full text access may be available.

To access full text, please use your member or institutional sign in.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
Bittanti, S. ; Dipartimento di Elettronica, Politecnico di Milano, Italy ; Campi, M.

The performance of the recursive least squares algorithm with constant forgetting factor in the identification of time-varying parameters is studied in a stochastic framework. It is shown that the mean square tracking error keeps bounded if and only if the so-called covariance matrix of the algorithm is L1-bounded. Then, a feasibility range for the forgetting factor is worked out in correspondence of which the covariance matrix (and therefore the tracking error) keeps bounded

Published in:

Automatic Control, IEEE Transactions on  (Volume:39 ,  Issue: 5 )