Adaptive radial basis function neural network control with variable variance parameters

International Journal of Systems Science

Volumn 32, Issue 4, 2001, Pages 413-424

  Chen, Shing Chia ^a   Chen, Wen Liang ^a  

^a NATIONAL TSING HUA UNIVERSITY   (Taiwan)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; ASYMPTOTIC STABILITY; COMPUTER SIMULATION; FEEDBACK CONTROL; LINEARIZATION; LYAPUNOV METHODS; NONLINEAR CONTROL SYSTEMS; RADIAL BASIS FUNCTION NETWORKS;

ADAPTIVE RADIAL BASIS FUNCTION NEURAL NETWORK CONTROL; FEEDBACK LINEARIZATION CONTROL;

ADAPTIVE CONTROL SYSTEMS;

EID: 0035323562     PISSN: 00207721     EISSN: 14645319     Source Type: Journal    
DOI: 10.1080/00207720119654     Document Type: Article

References (21)

1. Baum, E., and Haussler, D., 1989, What size net gives valid generation? Neural Computation, 1, 151-160.
2. Chen, C. S., and Chen, W. L., 1996, Robust model reference adaptive control of nonlinear systems using fuzzy systems. International Journal of Systems Science, 27, 1435-1442.
3. Chen, F. C., and Liu, C. C., 1994, Adaptively controlling nonlinear continuous-time systems using multilayer neural networks. IEEE Transactions on Automatic Control, 39, 1306-1310.
4. Ge, S. S., Hang, C. C., and Zhang, T., 1998 a, Neural-based adaptive control design for general nonlinear systems and its application to process control. Proceedings of the American Control Conference, Philadelphia, Pennsylvania, USA, June 1998, pp. 73-77; 1998 b, A direct approach to adaptive controller design and its application to inverted pendulum tracking. Proceedings of the American Control Conference, Philadelphia, Pennsylvania, USA, June 1998, pp. 1043-1047.
5. Ioannou, P. A., and Sun, J., 1996, Robust Adaptive Control (Englewood Cliff's, New Jersey: Prentice-Hall).
6. Isidori, A., 1989, Nonlinear Control Systems (New York: SpringerVerlag).
7. Jang, J.-S. R., Sun, C.-T., and Mizutani, E., 1997, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence (Upper Saddle River, New Jersey: Prentice Hall).
8. KadirkamanatHan, V., 1994, A statistical inference ased growth criteria for the RBF network. Proceedings of the IEEE Workshop on Neural Networks for Signal Processing, Porto, Ermioni, Greece, September 1994 (New York: IEEE), pp. 12-21.
9. Lee, S., and Kil, R. M., 1991, A Gaussian potential function network with hierarchically self-organizing learning. Neural Networks, 4, 207-224.
10. Liu, G. P., Kadirkamanathan, V., and Inings, S. A., 1999, Variable neural networks for adaptive control of nonlinear systems. IEEE Transactions on Systems, Man, and Cybernetics, Pt C; 29, 3443.
11. Park, J., and Sandberg, I. W., 1991, Universal approximation using radial-basis-function networks. Neural Computation, 3, 246-257.
12. Park, J., and Sandberg, I. W., 1993, Approximation and radialbasis-function networks. Neural Computation, 5, 305-316.
13. Sanner, R. M., and Slotine, J. E., 1992, Gaussian networks for direct adaptive control. IEEE Transactions on Neural Networks, 3, 837-863.
14. Slotine, J. E., and Li, W., 1991, Applied Nonlinear Control (Englewood Cliffs, New Jersey, Prentice-Hall).
15. Spooner, J. T., and Passino, K. M., 1996, Stable adaptive control using fuzzy systems and neural networks. IEEE Transactions on Fuzzy Systems, 4, 339-359.
16. Wang, G. J., and Hyang, J. U., 1999, RBFN-type neural-fuzzy networks. Proceedings of the 1999 Automatic Control Conference, Taichung, Taiwan, March 1999, pp. 278-283.
17. Wang, L. X., 1994, Adaptive Fuzzy Systems and Control: Design and Stability Analysis (Englewood Cliffs, New Jersey: Prentice-Hall).
18. Wettsctereck, D., and Dietterich D, 1992, Improving the performance of radial function networks by learning centre locations. Advances in Neural Information Processing System, 4, 1133-1140.
19. Yesidirek, A., and Lewis, F. L., 1995, Feedback linearization using neural networks, Automatica, 31, 1659-1664.
20. Zhang, T., Ge, S. S., and Hang, C. C., 1997, Neural-based direct adaptive control for a class of general nonlinear systems. International Journal of Systems Science, 28, 1011-1020; 1998 a, Direct adaptive control of non-affine nonlinear systems using multilayer neural networks. Proceedings of the American Control Conference, Philadelphia, Pennsylvania, USA, June 1998, pp. 515 519; 1998 b, Adaptive output feed ack control for general nonlinear systems using multilayer neural networks. Proceedings of the American Control Conference, Philadelphia, Pennsylvania, USA, June 1998, pp. 520-524; 1998c, Design and performance analysis of a direct adaptive controller for feedback linearizable systems. Proceedings of the American Control Conference, Philadelphia, Pennsylvania, USA, June 1998, pp. 3746-3750.
21. Zhihong, M., Wu, H. R., and Palaniswami, M., 1998, An adaptive tracking controller using neural networks for a class of nonlinear systems. IEEE Transactions on Neural Networks, 9, 947-955.