Convergence of least squares identification algorithms applied to unstable stochastic processes

1 June 1977

journal article
research article
Published by Taylor & Francis in International Journal of Systems Science

Vol. 8 (6) , 612-618
https://doi.org/10.1080/00207727708942067

Abstract

In this paper we prove the convergence of least squares identification algorithms when applied to unstable signal models. The proof is in terms of the properties of infinite sequences of matrices and of their norms to show that the convergence of least squares identification algorithms applies to unstable deterministic and stochatics processes.

Keywords

This publication has 7 references indexed in Scilit:

A unified sequential identification structure based on convergence considerations
Automatica, 1976
Derivation of ARMA parameters and orders from pure AR models
International Journal of Systems Science, 1975
On-line identification of linear dynamic systems with applications to Kalman filtering
IEEE Transactions on Automatic Control, 1971
Optimal Non-stationary Estimation of the Parameters of a Linear System with Gaussian Inputs †
Journal of Electronics and Control, 1963
New Results in Linear Filtering and Prediction Theory
Journal of Basic Engineering, 1961
The Fitting of Time-Series Models
Revue de l'Institut International de Statistique / Review of the International Statistical Institute, 1960
On the Statistical Treatment of Linear Stochastic Difference Equations
Econometrica, 1943