Neural regulator design

Neural Networks

Volumn 11, Issue 9, 1998, Pages 1695-1709

  Ahmed, M S ^a   Al Dajani, M A ^b  

^a KING FAHD UNIVERSITY OF PETROLEUM AND MINERALS   (Saudi Arabia)

^b UNIVERSITY OF CALIFORNIA   (United States)

Author keywords

Control system; Gradient scheme; Neural network; Nonlinear systems; Optimization; Regulation

Indexed keywords

FEEDFORWARD NEURAL NETWORKS; LEARNING SYSTEMS; MULTILAYER NEURAL NETWORKS; NONLINEAR CONTROL SYSTEMS; OPTIMIZATION;

NEURAL NETWORK-BASED REGULATORS;

RECURRENT NEURAL NETWORKS;

ANALYTICAL ERROR; ARTICLE; ARTIFICIAL NEURAL NETWORK; COMPUTER AIDED DESIGN; COMPUTER MODEL; CONTROL SYSTEM; MATHEMATICAL COMPUTING; NONLINEAR SYSTEM; PRIORITY JOURNAL; STOCHASTIC MODEL;

EID: 0032447634     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(98)00097-5     Document Type: Article

References (16)

1. Ahmed M. S. Block partial derivatives and its application to neural-net based direct-model-reference adaptive control. IEE Proc. Part D (Control Theory and Applications). 141(5):1994;315-322.
2. Ahmed M. S. BPD computation and model reference adaptive control of Hammerstein plants. IEE Proc. Part D (Control Theory and Applications). 142(5):1995;475-485.
3. Ahmed M. S., Anjum M. F. Neural net based direct self-tuning control of stochastic nonlinear plants. Int. J. of Control. 66(1):1997;85-104.
4. Astrom, K.J., & Wittenmark, B. (1990). Computer-controlled system. Englewood Cliffs, NJ: Prentice-Hall Inc.
5. Bryson, A.E. Jr., & Ho, Y. (1969). Applied optimal control. New York: Blaisdell.
6. Cybenko G. Approximation by superposition of a sigmoidal function. Math Control Signal Systems. 2:1989;303-314.
7. Dorf, R.C. (1992). Modern control systems. New York: Addison-Wesley Publishing Company.
8. Haykin, S. (1994). Neural networks: a comprehensive foundation. Toronto: Macmillan Publishing.
9. Kirk, D.E. (1970). Optimal control theory. Englewood Cliffs, NJ: Prentice-Hall Inc.
10. Levin A.V., Narendra K.S. Control of nonlinear dynamical systems using neural network - Part II: observability, identification and control. IEEE Trans. Neural Networks. 7(1):1996;30-42.
11. Lewis F.L., Liu K., Yesildirek A. Neural net robot controller with guaranteed tracking performance. IEEE Trans. Neural Networks. 6(3):1995;691-702.
12. Narendra K.S., Parthasarathy K. Identification and control of dynamic systems using neural networks. IEEE Trans. Neural Networks. 1(1):1990;4-27.
13. Rumelhart, D.E., Hinton, G.E., & Williams, R.J. (1986). Learning internal representation by back propagation. In Parallel distributed processing: exploration in the microstructure of cognition (Vol. 1). Cambridge, MA: Bradford Books.
14. Stinchcomb M., Hornik K., White H. Multilayered feedforward networks are universal approximators. Neural Networks. 2:1989;359-366.
15. Werbos, P.J. (1974). Beyond regression: new tools for prediction and analysis in the behavioral sciences. Ph.D. Thesis, Committee of Applied Mathematics, Harvard University.
16. Werbos P.J. Backpropagation through time: what it does and how to do it. Proc. of IEEE. 78:1990;1550-1560.