Weighted least squares algorithm for continuous-time model

Author

Gao, A.J.

Author_Institution

Dept. of Math., Kansas Univ., Lawrence, KS, USA

Volume

1

fYear

1994

Firstpage

263

Abstract

In this paper we discuss a weighted least squares algorithm for the following continuous-time model: A(S)y_t=SB(S)u_t+C(S)v_t where S denotes the integral operator, i.e. Sy_t=∫₀^ty_sds and A(S), B(S) and C(S) are matrix polynomials in the integral operator S. Similar to discrete WLS, the almost sure boundedness and convergence are established for continuous-time weighted least squares (CWLS) algorithm. The simulation results for the discrete WLS and the discrete extended least squares (ELS), the CWLS and the continuous ELS algorithms are given in this paper. Our techniques of proofs are totally different from those in discrete case. The results presented can be extended to state-space models with continuous time

Keywords

autoregressive moving average processes; continuous time systems; convergence of numerical methods; identification; least squares approximations; almost sure boundedness; continuous ARMAX model; continuous-time model; continuous-time weighted least squares; convergence; discrete extended least squares; identification; integral operator; matrix polynomials; state-space models; weighted least squares algorithm; Algorithm design and analysis; Convergence; Integral equations; Least squares methods; Mathematical model; Mathematics; Parameter estimation; Polynomials; Stochastic processes; Stochastic systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 1994., Proceedings of the 33rd IEEE Conference on

Conference_Location

Lake Buena Vista, FL

Print_ISBN

0-7803-1968-0

Type

conf

DOI

10.1109/CDC.1994.410920

Filename

410920