Subspace Identification of Pure Stochastic Systems

Author

Sendrescu, D. ; Popescu, D. ; Bobasu, E. ; Petre, E.

Author_Institution

Dept. of Autom. Control, Craiova Univ.

Volume

1

fYear

2006

fDate

25-28 May 2006

Firstpage

37

Lastpage

42

Abstract

In this paper we treat the subspace identification of pure stochastic systems with no external input. The stochastic identification problem consists of computing the stochastic system matrices from given output data only. We show how this can be done using geometric operations as orthogonal projections. Implementation of the subspace identification algorithm for stochastic systems has been discussed in terms of the numerically stable and efficient singular value decomposition. The proposed algorithm is tested on a simple example simulated with a Monte Carlo experiment

Keywords

Monte Carlo methods; identification; singular value decomposition; stochastic systems; Mote Carlo experiment; orthogonal projections; pure stochastic systems; singular value decomposition; stochastic identification problem; stochastic system matrices; subspace identification; Convergence of numerical methods; Covariance matrix; Iterative algorithms; Linear algebra; Proportional control; Singular value decomposition; Statistics; Stochastic processes; Stochastic systems; System identification;

fLanguage

English

Publisher

ieee

Conference_Titel

Automation, Quality and Testing, Robotics, 2006 IEEE International Conference on

Conference_Location

Cluj-Napoca

Print_ISBN

1-4244-0360-X

Electronic_ISBN

1-4244-0361-8

Type

conf

DOI

10.1109/AQTR.2006.254493

Filename

4022815