Parametric and nonparametric curve fitting

Automatica

Volumn 42, Issue 11, 2006, Pages 1869-1873

  Hsu, Kenneth ^a   Novara, Carlo ^b   Vincent, Tyrone ^c   Milanese, Mario ^b   Poolla, Kameshwar ^a  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b POLITECNICO DI TORINO   (Italy)

^c Department of Metallurgical and Materials Engineering   (United States)

Author keywords

Convergence; Curve fitting; Identifiability; Persistence of excitation; Static nonlinearity

Indexed keywords

CONVERGENCE OF NUMERICAL METHODS; FUNCTIONS; NONLINEAR EQUATIONS; PARAMETER ESTIMATION;

IDENTIFIABILITY; PERSISTENCE OF EXCITATION; STATIC NONLINEAR FUNCTIONS; STATIC NONLINEARITY;

CURVE FITTING;

EID: 33748950453     PISSN: 00051098     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.automatica.2006.05.024     Document Type: Article

References (20)

1. Anderson T.W., and Taylor J.B. Strong consistency of least squares estimates in normal linear regression. The Annals of Statistics 4 (1976) 788-790
2. Anderson T.W., and Taylor J.B. Strong consistency of least squares estimates in dynamic models. The Annals of Statistics 7 (1979) 484-489
3. Åström, K. J. (1985). The adaptive nonlinear modeler. Report TFRT-3178, Lund Institute of Technology.
4. Forssell U., and Ljung L. Some results on optimal experiment design. Automatica 36 (2000) 749-756
5. Johansen T.A. Identification of nonlinear systems using empirical data and prior knowledge-an optimization approach. Automatica 32 (1996) 337-356
6. Juditsky A., Hjalmarsson H., Benveniste A., Delyon B., Ljung L., Sjöberg J., et al. Nonlinear black-box models in system identification: Mathematical foundations. Automatica 31 (1995) 1725-1750
7. 1 system identification. Proceedings of the 1992 conference on decision and control.
8. Kolmogorov, A. (1930). Sur la loi forte des grandes nombres. Comptes Rendus de l Academie des Sciences Paris 191, 910-912.
9. Lai, T. L., Robbins, H., & Wei, C.Z. (1978). Strong consistency of least squares estimates in multiple regression. Proceedings of the national academy of sciences, Vol. 75, pp. 3034-3036.
10. Lancaster P., and Salkauskus K. Curve and surface fitting: An introduction (1986), Academic Press, London
11. Ljung, L. (1975). Theorems for asymptotic analysis of recursive stochastic algorithms. Report 7522, Lund Institute of Technology, Department of Automatic Control.
12. Ljung L. Analysis of recursive stochastic algorithms. IEEE Transactions on Automatic Control 22 (1977) 551-575
13. Ljung L. Convergence analysis of parametric identification methods. IEEE Transactions on Automatic Control 23 (1978) 770-783
14. Ljung L. System identification theory for the user. second ed. (1999), Prentice-Hall, Upper Saddle River, NJ
15. Ljung L., and Glad T. On global identifiability for arbitrary model parameterizations. Automatica 30 (1994) 265-276
16. Mehra R. Optimal input signals for parameter estimation in dynamic systems-survey and new results. IEEE Transactions on Automatic Control 19 (1974) 753-768
17. Sjöberg J., Zhang Q., Ljung L., Benveniste A., Delyon B., Glorennec P.Y., et al. Nonlinear black-box modeling in system identification: A unified overview. Automatica 31 (1995) 1691-1724
18. Stoica P., and Söderström T. Instrumental variable methods for identification of hammerstein systems. International Journal of Control 35 (1982) 459-576
19. Stone C.J. Consistent nonparametric regression. The Annals of Statistics 5 (1977) 595-620
20. Wigren T. Recursive prediction error identification using the nonlinear wiener model. Automatica 29 (1993) 1011-1025