Covariance analysis, positivity and the Yakubovich-Kalman-Popov lemma

Author

Johansson, Rolf ; Robertsson, Anders

Author_Institution

Dept. of Autom. Control, Lund Inst. of Technol., Sweden

Volume

4

fYear

2000

fDate

2000

Firstpage

3363

Abstract

This paper presents theory and algorithms for covariance analysis and stochastic realization without any minimality condition imposed. Also without any minimality conditions, we show that several properties of covariance factorization and positive realness hold. The results are significant for validation in system identification of state-space models from finite input-output sequences. Using the Riccati equation, we have designed a procedure to provide a reduced-order stochastic model that is minimal with respect to system order as well as the number of stochastic inputs

Keywords

Popov criterion; Riccati equations; covariance analysis; identification; state-space methods; Riccati equation; Yakubovich-Kalman-Popov lemma; covariance analysis; covariance factorization; finite I/O sequences; finite input-output sequences; positive realness; positivity; reduced-order stochastic model; state-space models; stochastic realization; system identification; Algorithm design and analysis; Analysis of variance; Covariance matrix; Data mining; Mathematical model; Riccati equations; Stochastic processes; Stochastic systems; System identification; Technological innovation;

fLanguage

English

Publisher

ieee

Conference_Titel

Decision and Control, 2000. Proceedings of the 39th IEEE Conference on

Conference_Location

Sydney, NSW

ISSN

0191-2216

Print_ISBN

0-7803-6638-7

Type

conf

DOI

10.1109/CDC.2000.912222

Filename

912222