Model-based recurrent neural network for modeling nonlinear dynamic systems

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics

Volumn 30, Issue 2, 2000, Pages 344-351

  Gan, Chengyu ^a   Danai, Kourosh ^a  

^a University of Massachusetts   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MATHEMATICAL MODELS; NONLINEAR CONTROL SYSTEMS; STATE SPACE METHODS; TOPOLOGY;

GAUSSIAN RADIAL BASIS FUNCTIONS (RBF); MODEL-BASED RECURRENT NEURAL NETWORKS (MBRNN);

RECURRENT NEURAL NETWORKS;

EID: 0033721588     PISSN: 10834419     EISSN: None     Source Type: Journal    
DOI: 10.1109/3477.836382     Document Type: Article

References (24)

1. O. Nerrand, P. Roussel-Ragot, D. Urbani, L. Personnaz, and G. Dreyfus, "Training recurrent neural networks: Why and how? An illustration in dynamical process modeling," IEEE Trans. Neural Networks, vol. 5, pp. 178-184, Feb. 1994.
2. J. Albus, "A new approach to manipulaor control: The cerebellar model articulation controller," ASME J. Dyn. Syst., Meas., and Control, vol. 97, pp. 220-227, 1975.
3. W. T. Miller, "Real time application of neural networks for sensor-based control of robots with vision," IEEE Trans. Syst., Man, Cybern., vol. 19, pp. 825-831, 1989.
4. A. G. parlos, K. T. Chong, and A. F. Atiya, "Application of the recurrent multilayer perceptron in modeling complex process dynamics," IEEE Trans. Neural Networks, vol. 5, pp. 255-266, Feb. 1994.
5. A. Srinivasan and C. Batur, "Back propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural models," IEEE Trans. Neural Networks, vol. 5, pp. 213-227, Feb. 1994.
6. B. Srinivasan, U. R. Prasad, and N. J. Rao, "Back propagation through adjoints for the identification of nonlinear dynamic systems using recurrent neural models," IEEE Trans. Neural Networks, vol. 5, pp. 213-227, 1994.
7. D. Obradovic, "On-line training of recurrent neural networks with continuous topology adaptation," IEEE Trans. Neural Networks, vol. 7, pp. 222-228, 1996.
8. C. C. Ku and K. Y. Lee, "Diagonal recurrent neural networks for dynamic systems control," IEEE Trans. Neural Networks, vol. 6, pp. 144-156, Jan. 1995.
9. T. Denoeux and R. Lengelle, "Initializing back propagation networks with prototypes," IEEE Trans. Neural Networks, vol. 6, pp. 351-363, 1993.
10. K. S. Narendra and K. Parthasarathy, "Identification and control of dynamical systems using neural networks," IEEE Trans. Neural Networks, vol. 1, pp. 4-27, Jan. 1990.
11. J. S. Jang and C. T. Sun, "Functional equivalence between radial basis function networks and fuzzy inference systems," IEEE Trans. Neural Networks, vol. 4, pp. 156-159, 1993.
12. K. J. Hunt, R. Haas, and R. Murray-Smith, "Extending the functional equivalence of radial basis function networks and fuzzy inference systems," IEEE Trans. Neural Networks, vol. 7, pp. 776-781, 1996.
13. G. G. Towell and J. W. Shavlik, "Knowledge-based artificial neural networks," Artif. Intell., vol. 70, pp. 110-165, 1994.
14. C. W. Omlin and C. L. Giles, "Rule revision with recurrent neural networks," IEEE Trans. Knowl. Data Eng., vol. 8, pp. 183-197, Jan. 1996.
15. M. M. Livstone, J. A. Farrell, and W. L. Baker, A computationally efficient algorithm for training recurrent connectionist networks, in ACC/WM2, pp. 555-561, 1992.
16. R. R. Hocking, "The analysis and selection of variables in linear regression," Biometrics, vol. 32, pp. 1-49, 1976.
17. R. R. Hocking, "Developments in linear regression methodology," Technometrics, vol. 25, pp. 219-249, 1983.
18. M. J. L. Orr, Introduction to Radial Basis Function Networks. London, U.K.: Centre for Cognitive Science, Univ. Edinburgh, 1996.
19. P. J. Werbos, "Backpropagation through time: What it does and how to do it," Proc. IEEE, vol. 78, pp. 1550-1560, Oct. 1990.
20. K. S. Narendra and K. Parthasarathy, "Gradient methods for the optimization of dynamical systems containing neural networks," IEEE Trans. Neural Networks, vol. 2, pp. 252-262, Feb. 1991.
21. A. Gelb, Ed., Applied Optimal Estimation. Cambridge, MA: MIT Press, 1974.
22. D. E. Rumelhart and J. L. McClelland, Eds., Parallel Distributed Processing: Explorations in Microstructure of Cognition. Cambridge, MA: MIT Press, 1986.
23. M. T. Hagan, H. B. Demuth, and M. Beale, Neural Network Design. Boston, MA: PWS, 1996.
24. F. L. Lewis, C. T. Abdallah, and D. M. Dawson, Control of Robot Manipulators. New York: Macmillan, 1993.