Neural Networks for Feedback Feedforward Nonlinear Control Systems

IEEE Transactions on Neural Networks

Volumn 5, Issue 3, 1994, Pages 436-449

  Parisini, T ^a   Zoppoli, R ^a  

^a UNIVERSITY OF GENOA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

CONSTRAINT THEORY; CONTROL THEORY; MATHEMATICAL MODELS; MATHEMATICAL PROGRAMMING; NONLINEAR CONTROL SYSTEMS; OPTIMAL CONTROL SYSTEMS; RECURSIVE FUNCTIONS; SET THEORY; STATE ESTIMATION; VECTORS;

FEEDBACK FEEDFORWARD NONLINEAR CONTROL SYSTEMS; FUNCTIONAL PROBLEMS; LINEAR STRUCTURE PRESERVING PRINCIPLE;

NEURAL NETWORKS;

EID: 0028425062     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.286914     Document Type: Article

References (24)

1. A. P. Sage, Optimum Systems Control. Englewood Cliffs, NJ: Prentice-Hall. 1968.
2. A. E. Bryson and Y. C. Ho, Applied Optimal Control. London, UK: Blaisdell, 1969.
3. R. E. Larson, State Increment Dynamic Programming. New York: American Elsevier Publishing Company. 1968.
4. T. Hanaoka. “Nonlinear control system design based on newly developed dynamic programming algorithm.” in Proc. First 1FAC Symp. on Design Methods of Control Systems, Zurich, Switzerland, 1991.
5. B. R. Eisenberg and A. P. Sage, “Closed-loop optimization of fixed configuration systems,” Int. J. of Control, vol. 3, pp. 183-194. 1966.
6. D. L. Kleinman and M. Athans, “The design of suboptimal linear time-varying systems,” IEEE Trans. Automat. Contr., vol. 13, pp. 150-159. 1968.
7. P. M. Makila and H. T. Toivonen. “Computational methods for parametric LQ problems—A survey,” IEEE Trans. Automat. Contr., vol. 32, pp. 658-671, 1987.
8. A. R. Barron, “Universal approximation bounds for superpositions of a sigmoidal function,” Tech. Rep. 58. Dept. Statistics, Univ. Illinois, 1991.
9. T. Parisini and R. Zoppoli, “Neural networks for the solution of N-stage optimal control problems,” in Artificial Neural Networks, T. Kohonen, K. Makisara, O. Simula, and J. Kangas, Eds. North-Holland, 1991.
10. T. Parisini and R. Zoppoli. “Multilayer neural networks for the optimal control of nonlinear dynamic systems,” in Proc. First IF AC Symp. Design Methods of Control Systems, Zurich. Switzerland, 1991.
11. T. Parisini and R. Zoppoli. “Backpropagation for N-stage optimal control problems,” in Proc. IJCNN '91, Singapore, 1991.
12. W. L. Brogan, Modern Control Theory. Englewood Cliffs, NJ: Prentice-Hall, 1991.
13. D. H. Nguyen and B. Widrow. “Neural networks for self-learning control systems,” IEEE Control System Mag., vol. 10, pp. 18-23, 1990.
14. K. Hornik, M. Stinchombe, and H. White, “Multilayer feedforward networks are universal approximators.” Neural Networks, vol. 2. pp. 359-366. 1989.
15. R. Hecht-Nielsen, “Theory of the backpropagation neural network,” in Proc. IJCNN '89, Washington, DC, 1989.
16. G. Cybenko, “Approximations by superpositions of a sigmoidal function,” Math. Control, Signals, Systems, vol. 2, pp. 303-314, 1989.
17. F. Girosi and G. Anzellotti, “Convergence rates of approximations by translates,” A.I. Memo No. 1288, M.I.T., Cambridge, MA, 1992.
18. Ya. Z. Tsypkin, Adaptation and Learning in Automatic Systems. New York: Academic Press, 1971.
19. B. T. Polyak and Ya. Z. Tsypkin, “Pseudogradient adaptation and training algorithms,” Automation and Remote Control vol. 12, pp. 377-397, 1973.
20. T. Tollenaere, “SuperSAB: fast adaptive backpropagation with good scaling properties,” Neural Networks, vol. 3, pp. 561-573, 1990.
21. D. E. Rumelhart and J. L. McClelland, Parallel Distributed Processing. Cambridge, MA: MIT Press, vol. 1, chapter 8, 1986.
22. P. J. Werbos, “Beyond regression: new tools for prediction and analysis in the behavioural sciences,” Ph.D. dissertation. Harvard University, Cambridge, MA, 1974.
23. Y. C. Ho and K. C. Chu, “Team decision theory and information structures in optimal control problems— Part I,” IEEE Trans. Automat. Contr., vol. 17, pp. 1-22, 1972.
24. M. Aicardi, G. Casalino, R. Minciardi, and R. Zoppoli, “On the stationary optimal receding-horizon strategies for dynamics teams with common past information structures,” IEEE Trans. Automat. Contr. vol. 37, pp. 1767-1771, 1992.