Title :
Adaptive RLS algorithms under stochastic excitation-L2 convergence analysis
Author :
Bittanti, Sergio ; Camp, Marco
Author_Institution :
Dipartimento di Elettronica, Politecnico di Milano, Italy
fDate :
8/1/1991 12:00:00 AM
Abstract :
A very general class of RLS (recursive least squares) algorithms having a 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 first given. Then, it is proved that the algorithm is exponentially convergent in the mean-square sense
Keywords :
convergence of numerical methods; filtering and prediction theory; identification; stochastic processes; adaptive RLS algorithms; convergence; forgetting factor; observation vector; recursive least squares; stochastic excitation; stochastic process; Adaptive control; Algorithm design and analysis; Convergence; Input variables; Least squares approximation; Least squares methods; Programmable control; Resonance light scattering; Stochastic processes;
Journal_Title :
Automatic Control, IEEE Transactions on
