A variable forgetting factor RLS algorithm with application to fuzzy time-varying systems identification

International Journal of Systems Science

Volumn 27, Issue 2, 1996, Pages 205-214

  Wang, Liang ^a   Langari, Reza ^a  

^a Texas A and M University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; CONVERGENCE OF NUMERICAL METHODS; ERROR ANALYSIS; FUZZY CONTROL; IDENTIFICATION (CONTROL SYSTEMS); LEAST SQUARES APPROXIMATIONS; MATHEMATICAL MODELS; PERFORMANCE; TIME VARYING CONTROL SYSTEMS;

FUZZY TIME VARYING SYSTEMS; RECURSIVE LEAST SQUARE; VARIABLE FORGETTING FACTOR;

ADAPTIVE ALGORITHMS;

EID: 0030083063     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/00207729608929205     Document Type: Article

References (19)

1. Anderson, B. D. O., and Moore, J. B., 1979, Optimal Filtering (Englewood Clifls, NJ: Prentice Hall).
2. Bierman, G. J., 1977, Factorization Methods for Discrete Sequential Estimation (New York: Academic Press).
3. Cordero, A. O., and Mayne, D. Q. 1981, Deterministic convergence of a self-tuning regulator with variable forgetting factor. Proceedings of the Institution of Electrical Engineers, Pt D, 128, 19-23.
4. Fortescue, T. R., Kershenbaum, L. S., and Ydstie, B. E., 1981, Implementation of self-tuning regulators with variable forgetting factors. Automatica, 17, 831-835.
5. Gooddwin, G. C., and Payne, R. L., 1977, Dynamic System Identification: Experiment Design and Data Analysis (New York: Academic Press).
6. Goodwin, G. C., Ramadge, P. J., and Caines, P. E., 1980, Discrete time multivariable adaptive control. IEEE Transactions on Automatic Control, 25, 449-456.
7. Goodwin, G. C., and Sin, K. S., 1984, Adaptive Filtering Prediction and Control (Englewood Cliffs, NJ: Prentice Hall).
8. Isermann, R., and Lachmann, K. H., 1985, Parameter-adaptive control with configuration aids and supervision functions. Automatica, 21, 625-638.
9. Kaminski, P. G., Bryson, A. E., and Schmidt, S. F., 1971, Discrete square-root filtering: a survey of current techniques. IEEE Transactions on Automatic Control, 16, 727-735.
10. LJUNG, L., 1987, Systems Identication: Theory for the User (Englewood Cliffs, NJ: Prentice Hall).
11. Ljung, L., and Gunnarsson, S., 1990, Adaptation and tracking in system identification: a survey. Automatica, 26, 7-21.
12. Moustafa, K. A. F., 1983, Identification of stochastic time-varying systems. Proceedings of the Institution of Electrical Engineers, Pt D, 130, 137-142.
13. Salgado, M. E., Goodwin, G. C., and Middleton, R. H., 1988, Modified least squares algorithm incorporating exponential resetting and forgetting. International Journal of Control, 47, 447-491.
14. Sugeno, M., and Kang, G. T., 1988, Structure identification of fuzzy model. Fuzzy Sets and Systems, 28, 15-33.
15. Surampudi, B., and Langari, R., 1993, A fuzzy cancellation of stick-slip in DC motors. Industrial Fuzzy Control Intelligent Systems, Houston, Texas, U.S.A., edited by R. Langari, J. Yen and J. Painter, pp. 24-29.
16. TakaGi, T., and Sugeno, M., 1985, Fuzzy identification of systems and its applications to modeling and control. IEEE Transactions on Systems, Man and Cybernetics, 15, 116-132.
17. Wang, L., and Langari, R., 1994 a, Complex systems modeling via fuzzy logic. IEEE Transactions on Systems, Man and Cybernetics
18. Wang, L., and Langari, R.,1994 b, Identification of fuzzy time-varying models (I): the deterministic case. Technical Report, Department of Mechanical Engineering, Texas A&M University.
19. Wang, L., Libert, G., and Manneback, P., 1992, Kalman filtering algorithm based on singular value decomposition. Proceedings of the 31st IEEE Control and Decision Conference, Tucson, Arizona, U.S.A., pp. 1224-1229.