Input-output modeling of nonlinear systems with time-varying linear models

IEEE Transactions on Automatic Control

Volumn 45, Issue 7, 2000, Pages 1355-1358

  Chowdhury, Fahmida N ^a  

^a UNIVERSITY OF LOUISIANA AT LAFAYETTE   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CONTROL SYSTEM ANALYSIS; CONTROL SYSTEM SYNTHESIS; CORRELATION METHODS; KALMAN FILTERING; REGRESSION ANALYSIS; STOCHASTIC CONTROL SYSTEMS; TIME DOMAIN ANALYSIS; TIME VARYING CONTROL SYSTEMS;

AUTO-REGRESSIVE MOVING AVERAGE (ARMA) MODELS; INPUT-OUTPUT MODELING; RANDOM WALK KALMAN FILTER (RWKF); TIME-VARYING LINEAR MODELS;

NONLINEAR CONTROL SYSTEMS;

EID: 0034223736     PISSN: 00189286     EISSN: None     Source Type: Journal    
DOI: 10.1109/9.867047     Document Type: Article

References (17)

1. A. Mu'awin and F. N. Chowdhury, "Extrapolative models of dynamic systems: Neural networks vs. Kalman filters," in Proc. 29th Southeastern Symp. Syst. Theory (SSST'97): IEEE Comp. Soc. Press, Mar. 1997, pp. 315-319.
2. R. B. Mrad, S. D. Fassois, and J. A. Levitt, "A polynomial algebraic method for nonstationary TARMA signal analysis," Signal Process., pt. I, vol. 65, pp. 1-19, 1998.
3. R. B. Mrad, S. D. Fassois, and J. A. Levitt, Signal Process., and Part II, pp. 21-38.
4. G. Kitagawa and W. Gersch, Smoothness Priors: Analysis of Time Series. New York: Springer, 1996, pp. 147-178. Lecture Notes in Statistics.
5. B. Abraham and J. Ledolter, Statistical Methods for Forecasting, 1983, pp. 364-368. Wiley Series in Prob. and Math. Statistics.
6. F. C. Schweppe, Uncertain Dynamic Systems. Englewood Cliffs, NJ: Prentice-Hall, 1973, pp. 432-433.
7. A. Gelb, Ed., Applied Optimal Estimation. Cambridge, MA: MIT Press, 1974, pp. 348-349.
8. E. Mosca, Optimal, Predictive, and Adaptive Control, T. Kailath, Ed. Englewood Cliffs, NJ: Prentice Hall Information and System Sciences Series, 1995.
9. I. J. Leontaritis and S. A. Billings, "Input-output parametric models for nonlinear systems," Int. J. Contr., vol. 41, no. 2, pp. 329-344, 1985.
10. G. Goodwin and K. Sin, Adaptive Filtering Prediction and Control, T. Kailath, Ed. Englewood Cliffs, NJ: Prentice Hall, 1984, pp. 262-263.
11. P. J. Werbos, T. McAvoy, and T. Su, "Neural networks, system identification, and control in the chemical process industries," in Handbook of Intelligent Control, D. White and D. Sofge, Eds. New York: Van Nostrand Reinhold, 1992, pp. 283-356.
12. F. N. Chowdhury, E. Lobo, X. Pei, and R. Thangavelu, "A neurostatistical method for fault detection in stochastic systems," in Proc. IEEE Conf. Contr. Applica., 1998, pp. 283-287.
13. P. M. Norgaard. (1998) Toolkit for Use with MATLAB. [Online]. Available: http://kalman.iau.dtu.dk/research/control/nnsysid.html
14. G. E. P. Box and G. M. Jenkins, Time Series Analysis: Forecasting and Control. San Francisco, CA: Holden-Day, 1976.
15. J. C. Spall, "Asymptotic distribution theory for the Kalman filter state estimator," Commun. Statist.-Theor. Meth., vol. 13, no. 16, pp. 1981-2003, 1984.
16. _, "The Kantorovich inequality for error analysis of the Kalman filter with unknown noise distributions," Automatica, vol. 31, no. 10, pp. 1513-1517, 1995.
17. A. Papoulis, Probability and Statistics. Englewood Cliffs, NJ: Prentice Hall, 1990, pp. 339-340.