Skip to main content
Have a personal or library account? Click to login
The role of parameter constraints in EE and OE methods for optimal identification of continuous LTI models Cover

The role of parameter constraints in EE and OE methods for optimal identification of continuous LTI models

Open Access
|Jun 2012

References

  1. Byrski, W. and Fuksa, S. (1995). Optimal identification of continuous systems inspace by the use of compact support filter,(4): 125-131.
  2. Byrski, W. and Fuksa, S. (1996). Linear adaptive controller for continuous system with convolution filter,, pp. 379-384.
  3. Byrski, W. and Fuksa, S. (1999). Time variable gram matrix eigen-problem and its application to optimal identification of continuous systems,, F0256.
  4. Byrski, W. and Fuksa, S. (2000). Optimal identification of continuous systems and a new fast algorithm for on line mode,, PM 2-5.
  5. Byrski, W. and Fuksa, S. (2001). Stability analysis of CLTI state feedback system with simultaneous state and parameter identification,, pp. 7-12.
  6. Byrski, W., Fuksa, S. and Byrski, J. (1999). A fast algorithm for the eigenproblem in on-line continuous model identification,, pp. 22-24.
  7. Byrski, W., Fuksa, S. and Nowak, M. (2003). The quality of identification for different normalizations of continuous transfer functions,, pp. 96-101.
  8. Co, T. and Ydstie, B. (1990). System identification using modulating functions and fast Fourier transforms,(10): 1051-1066.
  9. Eykhoff, P. (1974)., J. Wiley, London.
  10. Garnier, H. and Wang, L. (Eds.) (2008)., Advances in Industrial Control, Springer-Verlag, London.
  11. Gillberg, J. and Ljung, L. (2009). Frequency domain identification of continuous time ARMA models from sampled data,(6): 1371-1378.
  12. Johansson, R. (2010). Continuous-time model identification and state estimation using non-uniformly sampled data,, pp. 347-354.
  13. Ljung, L. and Wills, A. (2010). Issues in sampling and estimating continuous-time models with stochastic disturbances,(5): 925-931.
  14. Maletinsky, V. (1979). Identification of continuous dynamical systems with spline-type modulating functions method,, Vol. 1, p. 275.
  15. Preisig, H. A. and Rippin, D. W. T. (1993). Theory and application of the modulating function method,(1): 1-16.
  16. Schwartz, L. (1966)., Hermann, Paris.
  17. Shinbrot, M. (1957). On the analysis of linear and nonlinear systems,: 547-552.
  18. Sinha, N. K. and Kuszta, B. (1983)., Van Nostrand RC, New York, NY.
  19. Soderstrom, T. and Stoica, P. (1994)., Prentice Hall, London.
  20. Unbehauen, H. and Rao, G. P. (1987)., North-Holland, Amsterdam.
  21. Yeredor, A. (2006). On the role of constraints in system identification,, (see also http://diag.mchtr.pw.edu.pl/damadics).
  22. Young, P. (1981). Parameter estimation for continuous-time models—A survey,(1): 23-39.
DOI: https://doi.org/10.2478/v10006-012-0028-3 | Journal eISSN: 2083-8492 | Journal ISSN: 1641-876X
Language: English
Page range: 379 - 388
Published on: Jun 28, 2012
Published by: University of Zielona Góra
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2012 Witold Byrski, Jęedrzej Byrski, published by University of Zielona Góra
This work is licensed under the Creative Commons License.