Subspace identification by data orthogonalization and model decoupling

Automatica

Volumn 40, Issue 10, 2004, Pages 1689-1703

  Chiuso, Alessandro ^a   Picci, Giorgio ^a  

^a UNIVERSITY OF PADOVA   (Italy)

Author keywords

Exogenous inputs; Statistical analysis; Stochastic realization; Stochastic state space identification; Subspace identification

Indexed keywords

ALGORITHMS; BANDWIDTH; DATA REDUCTION; ERROR ANALYSIS; IDENTIFICATION (CONTROL SYSTEMS); INPUT OUTPUT PROGRAMS; PARAMETER ESTIMATION; REGRESSION ANALYSIS; STATISTICAL METHODS; STOCHASTIC CONTROL SYSTEMS; TRANSFER FUNCTIONS;

EXOGENOUS INPUTS; STOCHASTIC REALIZATION; STOCHASTIC STATE-SPACE IDENTIFICATION; SUBSPACE IDENTIFICATION;

AUTOMATA THEORY;

EID: 3843102545     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2004.05.008     Document Type: Article

References (22)

1. Akaike H. Markovian representation of stochastic processes by canonical variables. SIAM Journal of Control. 13:1975;162-173.
2. Bauer, D., Deistler, M., & Scherrer, W. (2000). On the impact of weighting matrices in subspace algorithms. In Proceedings of IFAC international symposium on system identification. Santa Barbara.
3. Caines, P. E., & Chan, C. W. (1976). Estimation, identification and feedback. In R. Mehra, & D. Lainiotis (Eds.), System identification: Advances and case studies (pp. 349-405). New York: Academic Press.
4. Chiuso, A., & Picci, G. (1999). Subspace identification by orthogonal decomposition. In Proceedings of the 14th IFAC World Congress, Vol. I. (pp. 241-246).
5. Chiuso, A., & Picci, G. (2000). Probing inputs for subspace identification. In Proceedings of the 2000 conference on decision and control. Sydney, Australia. pp. paper CDC00-INV0201.
6. Chiuso A., Picci G. Some algorithmic aspects of subspace identification with inputs. Applied Mathematics and Computer Sciences. 11(1):2001;55-76.
7. Chiuso A., Picci G. The asymptotic variance of subspace estimates. Journal of Econometrics. 118(1-2):2004;257-291.
8. Chiuso, A., & Picci, G. (2004b). Asymptotic variance of subspace methods by data orthogonalization and model decoupling: A comparative analysis. Automatica, this issue, doi: 10.1016/j.automatica.2004.05.009.
9. Chiuso A., Picci G. Numerical conditioning and asymptotic variance of subspace estimates. Automatica. 40(4):2004;677-683.
10. Chiuso A., Picci G. On the ill-conditioning of subspace identification with inputs. Automatica. 40(4):2004;575-589.
11. Desai U.B., Pal D., Kikpatrick R.D. A realization approach to stochastic model reduction. International Journal of Control. 42:1985;821-838.
12. Gevers M.R., Anderson B.D.O. Representation of jointly stationary feedback free processes. International Journal of Control. 33:1981;777-809.
13. Hannan E.J., Poskitt D.S. Unit canonical correlations between future and past. The Annals of Statistics. 16:1988;784-790.
14. Jansson M., Wahlberg B. On consistency of subspace methods for system identification. Automatica. 34:1998;1507-1519.
15. Kawauchi, H., Chiuso, A., Katayama, T., & Picci, G. (1999). Comparison of two subspace identification methods for combined deterministic -stochastic systems. In Proceedings of the 31st ISCIE international symposium on stochastic systems theory and its applications. Yokohama, Japan.
16. Lindquist A., Picci G. Canonical correlation analysis, approximate covariance extension and identification of stationary time series. Automatica. 32:1996;709-733.
17. Ljung L. System identification; theory for the user. 1997;Prentice-Hall, Englewood Cliffs, NJ.
18. Overschee, P. Van., & De Moor, B. (1996). Subspace identification for linear systems. Dordrecht: Kluwer Academic Publications.
19. Picci G., Katayama T. Stochastic realization with exogenous inputs and "subspace methods" identification. Signal Processing. 52:1996;145-160.
20. Picci G., Katayama T. Stochastic realization with exogenous inputs and "subspace methods" identification. Signal Processing. 52:1996;145-160.
21. Verhaegen M., Dewilde P. Subspace model identification, part 1. The output-error state-space model identification class of algorithms; part 2. Analysis of the elementary output-error state-space model identification algorithm. International Journal of Control. 56:1992;1187-1210 & 1211-1241.
22. Verhaegen M., Dewilde P. Subspace model identification, part 3. Analysis of the ordinary output-error state-space model identification algorithm. International Journal of Control. 58:1993;555-586