Title :
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
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;
Conference_Titel :
Decision and Control, 2000. Proceedings of the 39th IEEE Conference on
Conference_Location :
Sydney, NSW
Print_ISBN :
0-7803-6638-7
DOI :
10.1109/CDC.2000.912222
