Fast Neural Learning and Control of Discrete-Time Nonlinear Systems

IEEE Transactions on Systems, Man, and Cybernetics

Volumn 25, Issue 3, 1995, Pages 478-488

  Jin, Liang ^a   Nikiforuk, Peter N ^a   Gupta, Madan M ^a  

^a UNIVERSITY OF SASKATCHEWAN   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ALGORITHMS; COMPUTATIONAL COMPLEXITY; DISCRETE TIME CONTROL SYSTEMS; IDENTIFICATION (CONTROL SYSTEMS); LEARNING SYSTEMS; LINEARIZATION; MATHEMATICAL MODELS; NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS;

DISCRETE TIME NONLINEAR SYSTEMS; FEEDFORWARD CONNECTIONS; WEIGHT LEARNING ALGORITHM;

CONTROL SYSTEM ANALYSIS;

EID: 0029271757     PISSN: 00189472     EISSN: 21682909     Source Type: Journal    
DOI: 10.1109/21.364860     Document Type: Article

References (32)

1. D. E. Rumelhart and J. L. McCelland, “Learning internal representations by error propagation,” Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Vol. 1: Foundations, MIT Press, 1986.
2. R. Hecht-Nielsen, “Theory of the back-propagation neural network,” Proc. Int'l. Joint Conf. on Neural Networks, pp. I593–I605, June 1989.
3. P. K. Simpson, Artifical Neural Systems, Pergamon Press, 1990.
4. K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, pp. 359-366, 1989.
5. B. Widrow and M. A. Lehr, “30 years of adaptive neural networks: perception, madaline, and backpropagation,” Proc. IEEE, vol. 78, no. 9, pp. 1415–1441, 1990.
6. D. F. Specht, “A general regression neural network,” IEEE Trans. Neural Networks, vol. 2, no. 6, pp. 568–576, 1991.
7. S. Kollias and D. Anastassiou, “An adaptive least square algorithm for the efficient training of artificial neural network,” IEEE Trans. Circuit Syst., vol. 36, no. 8, pp. 1092–1101, 1989.
8. S. Singhal and L. Wu, “Training multilayer perceptions with the extended Kalman algorithm,” Advances in Neural Information Processing Systems I. San Mateo, CA: Morgan Kaufmann, 1989, pp. 133–140.
9. S. C. Douglas and T. H. Y. Meng, “Linearized least-squares training of multilayer feedforward neural networks,” Proc. IJCNN, vol. I, pp. 307–312, June 1991.
10. K. S. Narendra and K. Parthasarathy, “Gradient methods for the optimization of dynamical systems containing neural networks,” IEEE Trans. Neural Networks, vol. 2, no. 2, pp. 4–27, 1991.
11. L. Jin, P. N. Nikiforuk, and M. M. Gupta, “Weight-decoupled Kalman filter learning algorithm of multilayered neural networks,” Neural Networks World, (in press).
12. K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Trans. Neural Networks, vol. NN-1, no. 1, pp. 4–27, Mar., 1990.
13. S. Chen, C. F. N. Cowan, S. A. Billings, and P. M. Grant, “Parallel recursive prediction error algorithm for training layered neural networks,” Int. J. Control, vol. 51, no. 6, pp. 1215–1228, 1990.
14. S. A. Billings, H. B. Jamaluddin, and S. Chen, “Properties of neural networks with applications to modelling non-linear dynamical systems,” Int. J. Control, vol. 55, no. 1, pp. 193–224, 1992.
15. G. V. Puskorius and L. A. Feldkamp, “Decoupled extended Kalman filter training of feedforward layered networks,” Proceedings of 1991 IJCNN, vol. I, pp. 771–777, Seattle, 1991.
16. D. Psaltis, A. Sideris, and A. A. Yamamura, “A multilayered neural network controller,” IEEE Control System Mag., pp. 17–21, Apr. 1988.
17. F.-C. Chen, “Back-propagation neural networks for nonlinear self-tuning adaptive control,” IEEE Control System Mag., pp. 44–48, Apr. 1990.
18. K. J. Hunt and D. Sbarbaro, “Neural networks for nonlinear internal model control,” IEE Proc.-D, vol. 138, no. 5, pp. 431–438, Sept. 1991.
19. K. J. Hunt, D. Sbarbaro, R. Zbikowski, and P. J. Gawthrop, “Neural networks for control systems—A survey,” Automatica, vol. 28, no. 6, pp. 1083–1112, 1992.
20. L. Jin, P. N. Nikiforuk and M. M. Gupta, “Adaptive tracking of SISO nonlinear systems using multilayered neural networks,” Proc. of the 1992 American Control Conference, vol. 1, pp. 56–60, Chicago, June 24–26, 1992.
21. L. Jin, P. N. Nikiforuk and M. M. Gupta, “Direct adaptive output tracking control using multilayered neural networks,” IEE Proc.-D, vol. 140, no. 6, pp. 393–398, 1993.
22. R. M. Sanner and J. J. E. Slotine, “Gaussian networks for direct adaptive control,” IEEE Trans. Neural Networks, vol. 3, no. 6, pp. 837–863.
23. R. M. Sanner and J. J. E. Slotine, “Stable adaptive control and recursive identification using radial Gaussian network,” Proc. of the 30th CDC, vol. 2, pp. 2116–2123.
24. E. Tzirkel-Hancock and F. Fallside, “Stable control of nonlinear systems using neural networks,” International Journal of Robust and Nonlinear Control, vol. 2, no. 1, pp. 63–86, 1992.
25. S. Mukhopadhyay and K. S. Narendra, “Disturbance rejection in nonlinear systems using neural networks,” IEEE Trans. Neural Networks, vol. 4, no. 1, pp. 63–72, 1993.
26. W. T. Miller, II, R. S. Sutton, and P. J. Werbos, Ed., “Neural networks for control,” The MIT Press, London, England, 1990.
27. E. Hernandez and Y. Arkun, “Study of the control-relevant properties of backpropagation neural models of nonlinear dynamical systems,” Computer Chem. Engng., vol. 6, no. 4, pp. 227–240, 1992.
28. H. Sira-Ramirez, “Nonlinear discrete variable structure systems in quasi-sliding mode,” Int. J. Control, vol. 54, no. 5, pp. 1171–1187, 1991.
29. A. Isidori, Nonlinear Control System. New York: Springer Verlag, 1989.
30. H. Nijmeijer and A. J. Van der Schaft, Nonlinear Dynamical Control Systems. New York: Springer-Verlag, 1990.
31. L. Ljung and T. Soderstrom, Theory and Practice of Recursive Identification. Cambridge, MA: MIT Press, 1983.
32. A. H. Jazwinski, Stochastic Processes and Filtering Theory. New York: Academic Press, 1970.