Subspace-based system identification: Weighting and pre-filtering of instruments

Automatica

Volumn 38, Issue 3, 2002, Pages 433-443

  Gustafsson, Tony ^a  

^a CHALMERS UNIVERSITY OF TECHNOLOGY   (Sweden)

Author keywords

Asymptotic analysis; Identification algorithms; State space models; Subspace methods

Indexed keywords

ALGORITHMS; NORMAL DISTRIBUTION; OBSERVABILITY; OPTIMIZATION; STATE SPACE METHODS;

SUBSPACE METHODS;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 0036497639     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0005-1098(01)00235-7     Document Type: Article

References (14)

1. Analysis of the asymptotic properties of the MOESP type of subspace algorithms
2. A linear regression approach to state-space subspace system identification
3. On consistency of subspace methods for system identification
4. Realization of stochastic systems with exogenous inputs and subspace identification methods
5. System identification, reduced-order filtering and modeling via canonical variate analysis
6. Identification of the deterministic part of MIMO state space models given in innovations form from input-output data
7. Subspace-based methods for the identification of linear time-invariant systems
8. A statistical perspective on state-space modeling using subspace methods
9. Analysis of state space system identification methods based on instrumental variables and subspace fitting