A covariance matching approach for identifying errors-in-variables systems

Automatica

Volumn 45, Issue 9, 2009, Pages 2018-2031

  Söderström, Torsten ^a   Mossberg, Magnus ^b   Hong, Mei ^a  

^a UPPSALA UNIVERSITY   (Sweden)

^b KARLSTAD UNIVERSITY   (Sweden)

Author keywords

Covariance functions; Errors in variables models; Linear systems; System identification

Indexed keywords

ALTERNATIVE METHODS; BIAS-ELIMINATING LEAST SQUARES; COVARIANCE FUNCTIONS; COVARIANCE MATCHING; DYNAMIC ERROR; DYNAMIC SYSTEMS; ERRORS IN VARIABLES; ERRORS-IN-VARIABLES MODELS; ESTIMATION METHODS; IDENTIFICATION PROBLEM; INPUT AND OUTPUTS; INPUT SIGNAL; INPUT-OUTPUT; INPUT-OUTPUT DATA; NUMERICAL EXAMPLE; SYSTEM IDENTIFICATION;

DYNAMICAL SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); LINEAR SYSTEMS; NUMERICAL METHODS;

ERRORS;

EID: 68149163574     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2009.05.010     Document Type: Article

References (26)

1. Beghelli, S., Castaldi, P., Guidorzi, R., & Soverini, U. (1993). A robust criterion for model selection in identification from noisy data. In Proc. 9th international conference on systems engineering (pp. 480-484)
2. Beghelli S., Guidorzi R.P., and Soverini U. The Frisch scheme in dynamic system identification. Automatica 26 (1990) 171-176
3. Ding F., and Chen T. Hierarchical gradient-based identification of multivariable discrete-time systems. Automatica 41 2 (2005) 315-325
4. Ding F., and Chen T. Hierarchical least squares identification method for multivariable systems. IEEE Transactions on Automatic Control 50 3 (2005) 397-402
5. Ding F., Shi Y., and Chen T. Performance analysis of estimation algorithms of nonstationary ARMA processes. IEEE Transactions on Signal Processing 54 3 (2006) 1041-1053
6. Diversi, R., Guidorzi, R., & Soverini, U. (2003). A new criterion in EIV identification and filtering applications. In Proc. 13th IFAC symposium on system identification (pp. 1993-1998)
7. Diversi, R., Guidorzi, R., & Soverini, U. (2004). Frisch scheme-based algorithms for EIV identification. In Proc. 12th IEEE mediterranean conference on control and automation
8. Diversi, R., Soverini, U., & Guidorzi, R. (2006). Yule-Walker equations in the Frisch scheme solution of errors-in-variables identification problems. In Proc. 17th international symposium on mathematical theory of networks and systems (pp. 391-395)
9. Ekman, M. (2005). Identification of linear systems with errors in variables using separable nonlinear least squares. In 16th IFAC world congress
10. Ekman, M., Hong, M., & Söderström, T. (2006). A separable nonlinear least squares approach for identification of linear systems with errors in variables. In Proc. 14th IFAC symposium on system identification
11. Golub G.H., and Pereyra V. The differentiation of pseudoinverses and nonlinear least squares problems whose variables separate. SIAM Journal on Numerical Analysis 10 2 (1973) 413-432
12. Golub G.H., and Pereyra V. Separable nonlinear least squares: The variable projection method and its applications. Inverse Problems 19 2 (2003) R1-R26
13. Goodwin G.C., and Sin K.S. Adaptive filtering prediction and control (1984), Prentice Hall, Englewood Cliffs, NJ, USA
14. Hong M., and Söderström T. Relations between bias-eliminating least squares, the Frisch scheme and extended compensated least squares methods for identifying errors-in-variables systems. Automatica 45 1 (2009) 277-282
15. Ljung L., and Söderström T. Theory and practice of recursive identification (1983), MIT Press, Cambridge, MA, USA
16. Mossberg, M. (2008). Errors-in-variables identification through covariance matching: Analysis of a colored measurement noise case. In Proc. American control conference (pp. 1310-1315)
17. Söderström, T. (2006). On computing the Cramér-Rao bound and covariance matrices for PEM estimates in linear state space models. In Proc. 14th IFAC symposium on system identification
18. Söderström T. Errors-in-variables methods in system identification. Automatica 43 6 (2007) 939-958 Survey paper
19. Söderström T. Extending the Frisch scheme for errors-in-variables identification to correlated noise. International Journal of Adaptive Control and Signal Processing 22 1 (2008) 55-73
20. Söderström, T., Larsson, E. K., Mahata, K., & Mossberg, M. (2006). Using continuous-time modeling for errors-in-variables identification. In Proc. 14th IFAC symposium on system identification
21. Söderström T., and Stoica P. System identification (1989), Prentice Hall International, Hemel Hempstead, UK
22. Stoica P., Söderström T., Ahlén A., and Solbrand G. On the convergence of pseudo-linear regression algorithms. International Journal of Control 41 (1985) 1429-1444
23. In: Van Huffel S. (Ed). Recent advances in total least squares techniques and errors-in-variables modelling (1997), SIAM, Philadelphia, USA
24. In: Van Huffel S., and Lemmerling Ph. (Eds). Total least squares and errors-in-variables modelling. analysis, algorithms and applications (2002), Kluwer, Dordrecht, The Netherlands
25. Zheng W.X. Transfer function estimation form noisy input and output data. International Journal of Adaptive Control and Signal Processing 12 (1998) 365-380
26. Zheng W.X. A bias correction method for identification of linear dynamic errors-in-variables models. IEEE Transactions on Automatic Control 47 7 (2002) 1142-1147