Memory Neuron Networks for Identification and Control of Dynamical Systems

IEEE Transactions on Neural Networks

Volumn 5, Issue 2, 1994, Pages 306-319

  Sastry, P S ^a   Santharam, G ^a   Unnikrishnan, K P ^b,c  

^a INDIAN INSTITUTE OF SCIENCE   (India)

^b GENERAL MOTORS CORPORATION   (United States)

^c UNIVERSITY OF MICHIGAN   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE CONTROL SYSTEMS; ALGORITHMS; COMPUTER SIMULATION; DATA STORAGE EQUIPMENT; EQUIVALENCE CLASSES; IDENTIFICATION (CONTROL SYSTEMS); INPUT OUTPUT PROGRAMS; LEARNING SYSTEMS; MATHEMATICAL MODELS; NONLINEAR CONTROL SYSTEMS; SIGNAL FILTERING AND PREDICTION; SPEECH RECOGNITION;

DYNAMICAL SYSTEMS; FEED FORWARD NETWORKS; LEARNING ALGORITHMS; LINEAR FILTERS; MEMORY NEURON NETWORKS; TAPPED DELAY LINE; TRAINABLE TEMPORAL ELEMENTS;

NEURAL NETWORKS;

EID: 0028399792     PISSN: 10459227     EISSN: 19410093     Source Type: Journal    
DOI: 10.1109/72.279193     Document Type: Article

References (27)

1. W. T. Miller, R. S. Sutton, and P. J. Werbos, Neural Networks for Control. Cambridge, MA: MIT Press, 1990.
2. A. G. Barto, “Connectionist learning for control: An overview,” in Neural Neworks for Control W. T. Miller, R. S. Sutton, and P. J. Werbos, Eds. MIT Press, Cambridge, 1990.
3. K. S. Narendra and K. Parthasarathy, “Identification and control of dynamical systems using neural networks,” IEEE Transactions on Neural Networks, vol. 1, no. 1, pp. 4–27, 1990.
4. K. S. Narendra and K. Parthasarathy, “Gradient methods for optimization of dynamical systems containing neural networks,” IEEE Transactions on Neural Networks, vol. 2, no. 2, pp. 252–262, 1991.
5. Y. Ichikawa and T. Sawa, “Neural network application for direct feedback controllers,” IEEE Transactions on Neural Networks, vol. 3, no. 2, pp. 224–231, 1992.
6. P. Poddar and K. P. Unnikrishnan, “Memory neuron networks: A prolegomenon,” Tech. Rep. GMR-7493, General Motors Research Laboratories, 1991.
7. D. A. Hoskins, J. N. Hwang, and J. Vagners, “Iterative inversion of neural networks and its application to adaptive control,” IEEE Transactions on Neural Networks, vol. 3, no. 2, pp. 292–301, 1992.
8. Q. H. Wu, B. W. Hogg, and G. W. Irwin, “A neural network regulator for turbogenerators,” IEEE Transactions on Neural Networks, vol. 3, no. 1, pp. 95–100, 1992.
9. R. J. Williams, “Adaptive state representation and estimation using recurrent connectionist networks” in Neural Networks for Control W. T. Miller, R. S. Sutton, and P. J. Werbos, Eds. Cambridge, MA: MIT Press, 1990.
10. K. P. Unnikrishnan, J. J. Hopfield, and D. W. Tank, “Connected-digit speaker-dependent speech recognition system using a neural network with time-delayed connections,” IEEE Transactions on Accoustics Speech and Signal Processing, vol. 39, pp. 698–713, 1991.
11. P. Poddar and K. P. Unnikrishnan, “Efficient real-time prediction and recognition of temporal patterns,” in IEEE Workshop on Neural Networks for Signal Processing, Princeton, USA, October 1991.
12. S. Geman, E. Bienenstock and R. Doursat, “Neural networks and the bias/variance dilemma,” Neural Computation, vol. 4, pp. 1–58, 1992.
13. M. Kawato, “Computational schemes and neural network models for formation and control of multijoint arm trajectory,” in Neural Networks for Control W. T. Miller, R. S. Sutton, and P. J. Werbos eds. Cambridge, MA: MIT Press, 1990.
14. M. I. Jordan and D. E. Rumelhart, “Forward models: Supervised learning with a distal teacher,” Cognitive Science, vol. 16, pp. 307–354, 1992.
15. K. P. Unnikrishnan and K. P. Venugopal, “Alopex: A correlation-based learning algorithm for feedforward and recurrent neural networks,” in Neural Computation, vol. 6, pp. 467–488, 1994.
16. R. J. Williams and D. Zipser, “A learning algorithm for continually running recurrent neural networks,” Neural Computation, vol. 1, no. 2, pp. 270–280, 1989.
17. H. J. Kushner, Approximation and Weak Convergence Methods for Random Process, Cambridge, MA: MIT Press, 1984.
18. C.-M. Kuan and K. Homik, “Convergence of learning algorithms with constant learning rates,” IEEE Transactions on Neural Networks, vol. 2, pp. 484—489, 1991.
19. A. Beneveniste, M. Metivier, and P. Priouret, Adaptive Algorithms and Stochastic Approximations. New York: Springer Verlag, 1987.
20. P. S. Sastry, G. Santharam, and K. P. Unnikrishnan, “Memory neuron networks for identification and control of dynamical systems,” Tech. Rep. GMR-7916, General Motors Research Laboratories, 1993.
21. K. S. Narendra and A. M. Annaswamy, Stable Adaptive Systems. Englewood Cliffs, NJ: Prentice-Hall, 1989.
22. A. D. Back and A. C. Tsoi, “HR and HR synapses, a new neural network architecture for time series modeling” Neural Computation, vol. 3, no. 3, pp. 375–385, 1991.
23. S. P. Day and M. R. Davenport, “Continuous time temporal back propagation with adaptable time delays,” IEEE Transactions on Neural Networks, vol. 4, pp. 348–354, 1993.
24. R. R. Leighton and B. C. Conrath, “The autoregressive backpropagation algorithm,” in Proceedings of the International Joint Conference on Neural Networks, vol. 2, pp. 369–377, 1991.
25. O. Nerrand, P. Roussel-Ragot, L. Personnaz, G. Dreyfus, and S. Macros, “Neural networks and nonlinear adaptive filtering: Unifying concepts and new algorithms,” Neural Computation, vol. 5, pp. 165–199, 1993.
26. D. Nguyen and B. Widrow, “The truck backer-upper: An example of self-learning in neural networks,” in Neural Networks for Control W. T. Miller, R. S. Sutton, and P. J. Werbos, Eds. Cambridge, MA: MIT Press, 1990.
27. S. P. Meyn and P. E. Caines, “Asymptotic behavior of stochastic systems possessing Markovian realisations,” SIAM Journal of Control and Optimization, vol. 29, pp. 535–561, May 1991.