Some new trends in identification and modeling of nonlinear dynamical systems

Applied Mathematics and Computation

Volumn 78, Issue 2-3, 1996, Pages 279-297

  Unbehauen, H ^a  

^a RUHR UNIVERSITY BOCHUM   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 0002654422     PISSN: 00963003     EISSN: None     Source Type: Journal    
DOI: 10.1016/0096-3003(96)00015-X     Document Type: Article

References (31)

1. L. Ljung, System Identification - Theory for the User, Prentice-H all, Englewood Cliffs, NJ, 1987.
2. T. Söderström and P. Stoica, System Identification, Prentice-Hall, Englewood Cliffs, NJ, 1989.
3. H. Unbehauen, Control Engineering HI, Vieweg-Verlag, Braunschweig, 1995 (in German).
4. H. Unbehauen and G. P. Rao, Identification of Continuous Systems, North Holland, Amsterdam, 1987.
5. N. K. Sinha and G. P. Rao, Identification of Continuous-Time Systems, Kluwer Academic, Dordrecht, 1991.
6. U. Jedner and H. Unbehauen, Identification of a class of nonlinear systems by parameter estimation of a linear multi-model, in Proceedings, IMACS-Symposium on Modeling and Simulation for Control of Lumbed and Distributed Parameter Systems, Villeneuve, France, 1986, pp. 297-299.
7. M. Pottmann, H. Unbehauen, and D. Seborg, Application of a general multi-model approach for identification of highly nonlinear processes - A case study, Int. J. Control 57:97-120 (1993).
8. N. Wiener, Nonlinear Problems in Random Theory, MIT Press, Cambridge, MA, 1958.
9. I. W. Hunter and P. M. F. Nielson, Nonlinear and time-varying identification of living muscle cell mechanics, in Proceedings, 11th IFAC-World Congress, Tallinn, USSR, 1990, pp. 104-107.
10. D. Gabor, W. P. L. Wilby, and R. Woodcock, A universal nonlinear filter, simulator and predictor which optimizes itself by a learning process in IEE Proceedings, Part B, Vol. 108, pp. 422-433, 1961.
11. P. Eykhoff, System Identification, Wiley, London, 1974.
12. M. Kortmann and H. Unbehauen, Structure detection in the identification of nonlinear systems, in APH( Automatique-Productive-Informatique Industrielle), Vol. 22; pp. 5-25, 1988.
13. I. Leontaritis and S. Billings, Input-output parametric models for nonlinear systems (Parts I and II), Int. J. Control 41:303-344 (1985).
14. M. Kortmann and H. Unbehauen, Application of a recursive prediction error method to the identification of nonlinear systems using the Wiener model, in Proceedings, IMACS-Symposium on Modeling and Simulation for Control of Lumbed and Distributed Parameter Systems, Villeneuve, France, 1986, pp. 281-285.
15. C. Bányász, R. Haber, and L. Keviczky, Some estimation methods for nonlinear discrete-time identification, in Proceedings, 3rd IFAC-Symposium on Identification and System Parameter Estimation, Delft, Netherlands, 1973, pp. 793-802.
16. R. Haber and H. Unbehauen, Structure identification of nonlinear dynamic systems - A survey on input/output approaches; Automatica 16:651-677 (1990).
17. I. R. Horng and J. II. Chou, Analysis and identification of nonlinear systems via shifted Jacobi series, Int. J. Control 43:279-290 (1987).
18. V. R. Karanam, P. A. Frick, and R. R. Mohler, Bilinear system identification by Walsh functions, IEEE Trans. Automat. Control AC-23 (4):709-713 (1978).
19. R. Johansson, Real-time identification of multilinear systems, in Proceedings, 11th IFAC-World Congress, Tallinn, USSR, 1990, pp. 119-123.
20. A. E. Pearson and F. C. Lee, On the identification of polynomial input-output differential systems, IEEE Trans. Automat. Control AC-30 (8):778-782 (1985).
21. A. E. Pearson, Explicit parameter identification for a class of nonlinear input/output differential operator models, in Proceedings, 31st IEEE Control and Decision Conference, Tucson, Arizona, 1992, pp. 3656-3660.
22. A. Patra and H. Unbehauen, Identification of a class of nonlinear continuous-time systems using Hartley modulation functions, Int. J. Control, 62:1431-1451 (1995).
23. A. Patra and H. Unbehauen, Nonlinear modeling and identification, in Proceedings, IEEE/SM Conference, Le Touquet, France, 1993, pp. 441-445.
24. K. S. Narendra and K. Parthasarathy, Identification and control of dynamical systems using neural networks, IEEE Trans. Neural Networks NN-1:4-27 (1990).
25. S. Z. Quin, H. T. Su, and T. J. McAvoy, Comparison of four neural net learning methods for dynamic system identification, IEEE Trans. Neural Networks 3(1):122-130 (1992).
26. S. Chen, S. A. Billings, and P. M. Grant, Recursive hybrid algorithm for nonlinear system identification using radial basis function networks, Int. J. Control 55 (5): 1051-1070 (1992).
27. M. Pottmann and D. Seborg, Identification of nonlinear processes using reciprocal multiquadric functions, J. Process Control 2:189-203 (1992).
28. L. Wang, Adaptive Fuzzy Systems and Control: Design and Stability Analysis, PTR Prentice Hall, Englewood Cliffs, NJ, 1994.
29. T. Yin and C. Lee, Fuzzy model reference adaptive control, in Proceedings, 33rd IEEE-Control and Decision Conference, Orlando, FL, 1994, pp. 4130-4135.
30. U. Flunkert and H. Unbehauen, A nonlinear minimum-variance controller with application to a turbo-generator, in Proceedings, 32nd IEEE Control and Decision Conference, San Antonio, TX, 1993, pp. 1597-1601.
31. D. C. Saha and G. P. Rao, Identification of Continuous Dynamical Systems - The Poisson Moment Functional (PMF) Approach, Lecture Notes in Control and Information Science, Vol. 56, Springer-Verlag, Berlin, 1983.