Identification of ARX and ARARX models in the presence of input and output noises

European Journal of Control

Volumn 16, Issue 3, 2010, Pages 242-255

  Diversi, Roberto ^a   Guidorzi, Roberto ^a   Soverini, Umberto ^a  

^a UNIVERSITY OF BOLOGNA   (Italy)

Author keywords

ARARX models; ARX models; Dynamic frisch scheme; Errors in variables models; System identification

Indexed keywords

IDENTIFICATION (CONTROL SYSTEMS); INTELLIGENT SYSTEMS; MONTE CARLO METHODS; WHITE NOISE;

ARARX MODEL; ARX MODEL; AUTOREGRESSIVE MODELLING; DYNAMIC FRISCH SCHEME; EQUATION ERROR; ERROR-IN-VARIABLE MODEL; EXOGENOUS VARIABLES; INPUT AND OUTPUTS; SYSTEM-IDENTIFICATION; VARIABLE MODEL;

ERRORS;

EID: 77955407560     PISSN: 09473580     EISSN: None     Source Type: Journal    
DOI: 10.3166/ejc.16.242-255     Document Type: Article

References (23)

1. Aguero JC, Goodwin GC. Identifiability of errors in variables dynamic systems. Automatica 2008; 44: 371-382
2. Beghelli S, Guidorzi R, Soverini U. The Frisch scheme in dynamic system identification. Automatica 1990; 26: 171-176
3. Diversi R, Guidorzi R, Soverini U. Identification of ARX models with noisy input and output. In: Proceedings of the 9th European Control Conference, Kos, Greece, 2007, pp. 4073-4078
4. Diversi R, Guidorzi R, Soverini U. Identification of ARARX models in presence of additive noise. In: Proceedings of the 17th IFAC World Congress, Seoul, Korea, 2008, pp. 432-437
5. Durbin J. Efficient estimation of parameters in movingaverage models. Biometrika 1959; 46: 306-316
6. Fernando KV, Nicholson H. Identification of linear systems with input and output noise: the Koopmans- Levin method. IEE Proc 1985; 132: 30-36
7. Ghosh D. Maximum likelihood estimation of the dynamic shock-error model. J Econ 1989; 41: 121-143
8. Goodwin GC, Sin KS. Adaptive Filtering, Prediction and Control. Prentice-Hall, Englewood Cliffs, NJ, 1984
9. Guidorzi R. Multivariable System Identification: From Observations to Models. Bononia University Press, Bologna, Italy, 2003
10. th International Conference on Systems Science, Wroclaw, Poland, 1995, pp. 114-120
11. Krishnamurthy V. On-line estimation of dynamic shock-error models based on the Kullback-Leibler information measure. IEEE Trans Autom Control 1994; 39: 1129-1135
12. Ljung L. System Identification - Theory for the User. Prentice-Hall, Englewood Cliffs, NJ, 1999
13. Söderström T. Identification of stochastic linear systems in presence of input noise. Automatica 1981; 17: 713-725
14. Söderström T. Errors-in-Variables methods in system identification. Automatica 2007; 43: 939-958
15. Söderström T, Soverini U, Mahata K. Perspectives on errors-in-variables estimation for dynamic systems. Signal Proc 2002; 82: 1139-1154
16. Söderström T, Stoica P. System Identification. Prentice- Hall, Cambridge, UK, 1989
17. Söderström T, Stoica P, Friedlander B. An indirect prediction error method for system identification. Automatica 1991; 27: 183-188
18. Stoica P, Cedervall M, Eriksson T. Combined instrumental variable and subspace fitting approach to parameter estimation of noisy input-output systems. IEEE Trans Signal Proc 1995; 43: 2386-2397
19. Stoica P, Söderström T. Common factor detection and estimation. Automatica 1997; 33: 985-989
20. Tjärnström F, Ljung L. Variance properties of a two-step ARX estimation procedure. Eur J Control 2003; 9: 422-430
21. Van Huffel S (ed.). Recent Advances in Total Least Squares Techniques and Errors-in-Variables Modelling. SIAM, Philadelphia, PA, 1997.
22. Van Huffel S, Lemmerling P (eds.). Total Least Squares Techniques and Errors-in-Variables Modelling: Analysis, Algorithms and Applications. Kluwer Academic Publishers, Dordrecht, The Netherlands, 2002
23. Van Huffel S, Vandewalle J. Comparison of total least squares and instrumental variable methods for parameter estimation of transfer function models. Int J Control 1989; 50: 1039-1056