DocumentCode
1857702
Title
Mean square convergence of an adaptive RLS algorithm with stochastic excitation
Author
Bittanti, Sergio ; Campi, Marco
Author_Institution
Dept. of Electron., Polytech., Milano, Italy
fYear
1989
fDate
13-15 Dec 1989
Firstpage
1946
Abstract
The RLS (recursive least-squares) algorithm with forgetting factor is considered. The basic assumptions are that the data generation mechanism is free of disturbances and that the observation vector is a stochastic process satisfying a φ-mixing condition. A stochastic characterization of persistent excitation is given. It is proved that the algorithm is exponentially convergent in the mean-square sense
Keywords
convergence of numerical methods; least squares approximations; parameter estimation; state estimation; stochastic processes; φ-mixing condition; forgetting factor; least squares approximations; mean square convergence; observation vector; parameter estimation; persistent excitation; recursive least-squares; state estimation; stochastic excitation; stochastic process; Algorithm design and analysis; Convergence; Input variables; Least squares methods; Parameter estimation; Resonance light scattering; Silicon compounds; Stochastic processes; Vectors;
fLanguage
English
Publisher
ieee
Conference_Titel
Decision and Control, 1989., Proceedings of the 28th IEEE Conference on
Conference_Location
Tampa, FL
Type
conf
DOI
10.1109/CDC.1989.70504
Filename
70504
Link To Document