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
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;
Conference_Titel :
Decision and Control, 1989., Proceedings of the 28th IEEE Conference on
Conference_Location :
Tampa, FL
DOI :
10.1109/CDC.1989.70504
