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@article{IJAMCS_2004_14_2_a7,
author = {Ibrir, S. and Diop, S.},
title = {A numerical procedure for filtering and efficient high-order signal differentiation},
journal = {International Journal of Applied Mathematics and Computer Science},
pages = {201--208},
publisher = {mathdoc},
volume = {14},
number = {2},
year = {2004},
language = {en},
url = {http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_2_a7/}
}
TY - JOUR AU - Ibrir, S. AU - Diop, S. TI - A numerical procedure for filtering and efficient high-order signal differentiation JO - International Journal of Applied Mathematics and Computer Science PY - 2004 SP - 201 EP - 208 VL - 14 IS - 2 PB - mathdoc UR - http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_2_a7/ LA - en ID - IJAMCS_2004_14_2_a7 ER -
%0 Journal Article %A Ibrir, S. %A Diop, S. %T A numerical procedure for filtering and efficient high-order signal differentiation %J International Journal of Applied Mathematics and Computer Science %D 2004 %P 201-208 %V 14 %N 2 %I mathdoc %U http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_2_a7/ %G en %F IJAMCS_2004_14_2_a7
Ibrir, S.; Diop, S. A numerical procedure for filtering and efficient high-order signal differentiation. International Journal of Applied Mathematics and Computer Science, Tome 14 (2004) no. 2, pp. 201-208. http://geodesic.mathdoc.fr/item/IJAMCS_2004_14_2_a7/
[1] Anderson R.S. and Bloomfield P. (1974): A time series approach to numerical differentiation. — Technom., Vol. 16, No. 1, pp. 69–75.
[2] Barmish B.R. and Leitmann G. (1982): On ultimate boundness control of uncertain systems in the absence of matching assumptions.—IEEE Trans. Automat. Contr., Vol. AC-27, No. 1, pp. 153–158.
[3] Chen Y. H. (1990): State estimation for non-linear uncertain systems: A design based on properties related to the uncertainty bound.—Int. J. Contr., Vol. 52, No. 5, pp. 1131–1146.
[4] Chen Y. H. and Leitmann G. (1987): Robustness of uncertain systems in the absence of matching assumptions. — Int. J. Contr., Vol. 45, No. 5, pp. 1527–1542.
[5] Ciccarella G., Mora M.D. and Germani A. (1993): A Luenberger-like observer for nonlinear systems. — Int. J. Contr., Vol. 57, No. 3, pp. 537–556.
[6] Craven P. and Wahba G. (1979): Smoothing noisy data with spline functions: Estimation the correct degree of smoothing by the method of generalized cross-validation. — Numer. Math., Vol. 31, No.4, pp. 377–403.
[7] Dawson D.M., Qu Z. and Caroll J.C. (1992): On the state observation and output feedback problems for nonlinear uncertain dynamic systems. — Syst. Contr. Lett., Vol. 18, No.3, pp. 217–222.
[8] De Boor C., (1978): A Practical Guide to Splines.—New York: Springer.
[9] Diop S., Grizzle J.W., Morral P.E. and Stefanoupoulou A.G. (1993): Interpolation and numerical differentiation for observer design. — Proc. Amer. Contr. Conf., Evanston, IL, pp. 1329–1333.
[10] Eubank R.L. (1988): Spline Smoothing and Nonparametric Regression. — New York: Marcel Dekker.
[11] Gasser T., Müller H.G. and Mammitzsch V. (1985): Kernels for nonparametric curve estimation. — J. Roy. Statist. Soc., Vol. B47, pp. 238–252.
[12] Gauthier J.P., Hammouri H. and Othman S. (1992): A simple observer for nonlinear systems: Application to bioreactors. — IEEE Trans. Automat. Contr., Vol. 37, No. 6, pp. 875–880.
[13] Georgiev A.A. (1984): Kernel estimates of functions and their derivatives with applications.—Statist. Prob. Lett., Vol. 2, pp. 45–50.
[14] Härdle W. (1984): Robust regression function estimation. — Multivar. Anal., Vol. 14, pp. 169–180.
[15] Härdle W. (1985): On robust kernel estimation of derivatives of regression functions.—Scand. J. Statist., Vol. 12, pp. 233–240.
[16] Ibrir S. (1999): Numerical algorithm for filtering and state observation. — Int. J. Appl. Math. Comp. Sci., Vol. 9, No.4, pp. 855–869.
[17] Ibrir S. (2000): Méthodes numriques pour la commande et l’observation des systèmes non linéaires. — Ph.D. thesis, Laboratoire des Signaux et Systèmes, Univ. Paris-Sud.
[18] Ibrir S. (2001): New differentiators for control and observation applications. — Proc. Amer. Contr. Conf., Arlington, pp. 2522–2527.
[19] Ibrir S. (2003): Algebraic riccati equation based differentiation trackers. — AIAA J. Guid. Contr. Dynam., Vol. 26, No. 3, pp. 502–505.
[20] Kalman R.E. (1960): A new approach to linear filtering and prediction problems. — Trans. ASME J. Basic Eng., Vol. 82, No. D, pp. 35–45.
[21] Leitmann G. (1981): On the efficacy of nonlinear control in uncertain linear systems. — J. Dynam. Syst. Meas. Contr., Vol. 102, No.2, pp. 95–102.
[22] Luenberger D.G. (1971): An introduction to observers.—IEEE Trans. Automat. Contr., Vol. 16, No.6, pp. 596–602.
[23] Misawa E.A. and Hedrick J.K. (1989): Nonlinear observers. A state of the art survey. — J. Dyn. Syst. Meas. Contr., Vol.111, No.3, pp. 344–351.
[24] Müller H.G. (1984): Smooth optimum kernel estimators of densities, regression curves and modes.—Ann. Statist., Vol. 12, pp. 766–774.
[25] Rajamani R. (1998): Observers for Lipschitz nonlinear systems. — IEEE Trans. Automat. Contr., Vol. 43, No. 3, pp. 397–400.
[26] Reinsch C.H. (1967): Smoothing by spline functions.—Numer. Math., Vol. 10, pp. 177–183.
[27] Reinsch C.H. (1971): Smoothing by spline functions ii. — Numer. Math., Vol. 16, No.5, pp. 451–454.
[28] Slotine J.J.E., Hedrick J.K. and Misawa E.A. (1987): On sliding observers for nonlinear systems.—J. Dynam. Syst. Meas. Contr., Vol. 109, No.3, pp. 245–252.
[29] Tornambè A. (1992): High-gain observers for nonlinear systems.— Int. J. Syst. Sci., Vol. 23, No.9, pp. 1475–1489.
[30] Xia X.-H. and Gao W.-B. (1989): Nonlinear observer design by observer error linearization.—SIAM J. Contr. Optim., Vol. 27, No. 1, pp. 199–216.