Time variable parameter estimation

IFAC Proceedings Volumes (IFAC-PapersOnline)

Volumn 15, Issue PART 1, 2009, Pages 432-437

  Young, Peter C ^a,b,c  

^a LANCASTER UNIVERSITY   (United Kingdom)

^b AUSTRALIAN NATIONAL UNIVERSITY   (Australia)

^c UNIVERSITY OF NEW SOUTH WALES   (Australia)

Author keywords

Filtering; Non stationary; Recursive estimation; Smoothing; Time variable

Indexed keywords

AUTO-REGRESSIVE; ESTIMATION METHODOLOGIES; EXOGENOUS VARIABLES; FIXED-INTERVAL SMOOTHING; INSTRUMENTAL VARIABLES; LEAST SQUARES ESTIMATION; MODELLING TIME; NONSTATIONARY; RECURSIVE ESTIMATION; SMOOTHING; TIME-VARIABLE; TRANSFER FUNCTION MODEL;

ESTIMATION; IDENTIFICATION (CONTROL SYSTEMS); LEAST SQUARES APPROXIMATIONS; TRANSFER FUNCTIONS;

PARAMETER ESTIMATION;

EID: 80051643561     PISSN: 14746670     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.3182/20090706-3-FR-2004.0135     Document Type: Conference Paper

References (19)

1. S. Bittanti and M. Campi. Bounded error identification of time-varying parameters by rls techniques. IEEE Trans- actions on Automatic Control, 39:1106-1110, 1994.
2. A. E. Bryson and Y. C. Ho. Applied Optimal Control. Blaisdell, Waltham, Mass., 1969.
3. H. F. Chen and L. Guo. Identification and Stochastic Adaptive Control. Birkhauser, Boston, 1991.
4. A. P. Dempster, N. M. Laird, and D. B. Rubin. Maximum likelihood from incomplete data via the EM algorithm. Jnl. Royal Stat. Soc., Series B, 39:1-38, 1977.
5. A. C. Harvey. Forecasting Structural Time Series Models and the Kalman Filter. Cambridge University Press: Cambridge, 1989.
6. A. J. Jakeman and P. C. Young. Recursive filtering and the inversion of ill-posed causal problems. Utilitas Mathematica, 25:351-376, 1981.
7. G. Kitagawa and W. Gersch. Smoothness Priors Analysis of Time Series. Springer-Verlag, New York, 1996.
8. R. Kulhavy. Restricted exponential forgetting in real time identification. Automatica, 23:589-600, 1987. (Pubitemid 18532544)
9. J. P. Norton. An Introduction to Identification. Academic Press, New-York, 1986.
10. J. P. Norton. Optimal smoothing in the identification of linear time-varying systems. Proceedings Institute Electrical Engineers, 122:663-668, 1975.
11. F. Schweppe. Evaluation of likelihood functions for gaus- sian signals. IEEE Trans. on Information Theory, 11: 61-70, 1965.
12. C. J. Taylor, D. J. Pedregal, P. C. Young, and W. Tych. Environmental time series analysis and forecasting with the CAPTAIN toolbox. Environmental Modelling & Software, 22:797-814, 2007. (Pubitemid 46186097)
13. P. E. Wellstead and M. B. Zarrop. Self-Tuning Systems: Control and Signal Processing. John Wiley, New York, 1991.
14. P. C. Young. Recursive Estimation and Time-Series Analysis. Springer-Verlag, Berlin, 1984.
15. P. C. Young. Nonstationary time series analysis and forecasting. Progress in Environmental Science, 1:3-48, 1999.
16. P. C. Young. The refined instrumental variable method: unified estimation of discrete and continuous-time trans- fer function models. Journal Européen des Systèmes Automatisés, 42:149-179, 2008.
17. P. C. Young. Differential Equation Error Method of Real- Time Process Identification. Ph.D Thesis, University of Cambridge, Cambridge, U.K., 1970.
18. P. C. Young, D. J. Pedregal, and W. Tych. Dynamic harmonic regression. Jnl. of Forecasting, 18:369-394, 1999. (Pubitemid 129664243)
19. P. C. Young, P. McKenna, and J. Bruun. Identification of nonlinear stochastic systems by state dependent param- eter estimation. International Journal of Control, 74: 1837-1857, 2001.