An analysis of learning control by backpropagation through time

Neural Networks

Volumn 11, Issue 4, 1998, Pages 709-721

  Stroeve, Sybert ^a  

^a DELFT UNIVERSITY OF TECHNOLOGY   (Netherlands)

Author keywords

Backpropagation through time; Feedback control; Feedforward control; Inverse dynamics; Learning control; Neuromuscular control; Neuromusculoskeletal system

Indexed keywords

BACKPROPAGATION; CONTROL SYSTEM ANALYSIS; DATA STRUCTURES; FEEDBACK CONTROL; INTELLIGENT CONTROL; LEARNING SYSTEMS; LINEAR CONTROL SYSTEMS; MATHEMATICAL MODELS; NONLINEAR SYSTEMS;

BACKPROPAGATION THROUGH TIME; FEEDFORWARD CONTROL; INVERSE DYNAMICS; NEURAL CONTROL SYSTEM; NEUROMUSCULAR CONTROL; NEUROMUSCULOSKELETAL SYSTEM;

LEARNING ALGORITHMS;

ARM MOVEMENT; ARTICLE; CONTROL SYSTEM; FEEDBACK SYSTEM; LEARNING; MOTOR CONTROL; NERVE CELL NETWORK; NEUROMUSCULAR SYSTEM; PRIORITY JOURNAL;

EID: 0032096414     PISSN: 08936080     EISSN: None     Source Type: Journal    
DOI: 10.1016/S0893-6080(98)00011-2     Document Type: Article

References (21)

1. Agarwal M. A systematic classification of neural-network-based control. IEEE Control Systems. 1997:1997;75-93.
2. Beaufays F., Abdel-Magid Y., Widrow B. Application of neural networks to load-frequency control in power systems. Neural Networks. 7:1994;183-194.
3. Bizzi E., Accornero N., Chapple W., Hogan N. Posture control and trajectory formation during arm movement. J. Neurosci. 4(11):1984;2738-2744.
4. Flash T., Hogan N. The coordination of arm movements: an experimentally confirmed mathematical model. J. Neurosci. 7:1985;1688-1703.
5. Gomi H., Kawato M. Adaptive feedback control models of the vestibulocerebellum and spinocerebellum. Biol. Cybernet. 68:1992;105-114.
6. Gorinevsky D.M. Modelling of direct motor learning in fast human arm movements. Biol. Cybernet. 69:1993;219-228.
7. Happee, R., 1992. The control of shoulder muscles during goal directed movements. Ph.D. Thesis, Delft University of Technology, The Netherlands.
8. Heskes T.M., Kappen B. Learning-parameter adjustment in neural networks. Phys. Rev. A 45(12):1992;8885-8893.
9. Kawato M., Gomi H. A computational model for four regions of the cerebellum based on feedback-error-learning. Biol. Cybernet. 68:1992;95-103.
10. Kawato M., Furukawa K., Suzuki R. A hierarchical neural network model for control and learning of voluntary movement. Biol. Cybernet. 57:1987;169-185.
11. Lacquaniti F., Soechting J.F. EMG responses to load perturbations of the upper limb: effect of dynamics coupling between shoulder and elbow motion. Exp. Brain Res. 61:1986;482-496.
12. Leshno M., Lin V.Y., Pinkus A., Schocken S. Multilayer feedforward networks with a nonpolynomial activation function can approximate any function. Neural Networks. 6:1993;861-867.
13. Ljung, L., 1987. System Identification: Theory for the User. Prentice-Hall, Englewood Cliffs, NJ.
14. Narendra, K.S., Annaswamy, A.M., 1989. Stable Adaptive Systems. Prentice-Hall, Englewood Cliffs, NJ.
15. Nguyen D., Widrow B. Neural networks for self-learning control systems. IEEE Control Syst. Mag. 10(3):1990;18-23.
16. Park Y.M., Chol M.S., Lee K.Y. An optimal tracking neuro-controller for nonlinear dynamic systems. IEEE Trans. Neural Networks. 7:1996;1099-1110.
17. Stroeve S. Learning combined feedback and feedforward control of a musculoskeletal system. Biol. Cybernet. 75:1996;73-83.
18. Stroeve S. (1997a). A learning feedback and feedforward neuromuscular control model for two degrees of freedom arm movements. Human Movement Sci., 16 (5), 621-651.
19. Stroeve S. (1997b). Impedance characteristics of a neuromusculoskeletal model of the human arm: I Posture control. Biological Cybernetics, submitted.
20. Werbos P.J. Generalization of backpropagation with application to a recurrent gas market model. Neural Networks. 1:1988;339-356.
21. Winters J.M., Stark L. Analysis of fundamental human movement patterns through the use of in-depth antagonistic muscle model. IEEE Trans. Biomed. Engng. 32(10):1985;826-839.