Identification of errors-in-variables systems with ARMA observation noises

Systems and Control Letters

Volumn 57, Issue 5, 2008, Pages 420-424

  Song, Qi jiang ^a   Chen, Han Fu ^a  

^a INSTITUTE OF SYSTEMS SCIENCE   (China)

Author keywords

ARMA; Errors in variables; Identification; Recursive estimate; Stochastic approximation; Strong consistency

Indexed keywords

ACOUSTIC NOISE; APPROXIMATION ALGORITHMS; COMPUTER SIMULATION; RECURSIVE FUNCTIONS; STOCHASTIC CONTROL SYSTEMS;

ERRORS IN VARIABLES (EIV) SYSTEMS; RECURSIVE ESTIMATES; STOCHASTIC APPROXIMATION (SA); STRONG CONSISTENCY;

IDENTIFICATION (CONTROL SYSTEMS);

EID: 40949147377     PISSN: 01676911     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.sysconle.2007.10.010     Document Type: Article

References (17)

1. Anderson B.D.O. Identification of scalar errors-in-variables models with dynamics. Automatica 21 6 (1985) 709-716
2. Anderson B.D.O., and Deistler M. Identifiability in dynamic errors-in-variables models. Journal of Time Series Analysis 5 1 (1984) 1-13
3. J.C. Agüero, G.C. Goodwin, Identifiability of errors in variables dynamic systems, in: IFAC Symposium on System Identification, Newcastle, Australia, 2006
4. Castaldi P., and Soverini U. Identification of dynamic errors-in-variables models. Automatica 32 4 (1996) 631-636
5. Chen H.F., and Yang J.M. Strong consistent coefficient estimate for errors-in-variables models. Automatica 41 (2005) 1025-1033
6. H.F. Chen, Recursive identification of multivariate errors-in-variables systems, in: Chinese Control Conference, 2006
7. Chen H.F. Stochastic Approximation and its Applications (2002), Kluwer, Dordrecht
8. Chow Y.S., and Teicher H. Probability Theory (1978), Springer, New York
9. Hu X.L., and Chen H.F. Identification for Wiener systems with RTF subsystems. European Journal of Control 6 (2006) 581-594
10. Solo V. Identifiability of time series models with errors in variables. Journal of Applied Probability 23A (1986) 63-71
11. Stoica P., and Nehorai A. On the uniqueness of prediction error models for systems with noisy input-output data. Automatica 23 4 (1987) 541-543
12. Söderström T. Spectral decomposition with application to identification. Numerical Techniques for Stochastic Systems (1980), NorthHolland, Amsterdam
13. Söderström T., Mahata K., and Soverini U. Identification of dynamic errors-in-variables models: approaches based on two-dimensional ARMA modeling of the data. Automatica 39 (2003) 929-935
14. Söderström T., Hong M., and Zheng W.X. Convergence properties of bias-eliminating algorithm for errors-in-variables identification. International Journal of Adaptive Control and Signal Processing 19 9 (2005) 703-722
15. Stout W.F. Almost Sure Convergence (1974), Academic Press
16. Stoica P. Generalized Yule-Walker equations and testing the orders of multivariate time series. International Journal of Control 37 5 (1983) 1159-1166
17. Zheng W.X. A bias correction method for identification of linear dynamic errors-in-variables models. IEEE Transaction on Automatic Control 47 7 (2002) 1142-1147