Characteristics Modeling of Dynamic Systems Using Adaptive Neural Computation

- Journal title : Journal of Institute of Control, Robotics and Systems
- Volume 13, Issue 4, 2007, pp.309-314
- Publisher : Institute of Control, Robotics and Systems
- DOI : 10.5302/J.ICROS.2007.13.4.309

Title & Authors

Characteristics Modeling of Dynamic Systems Using Adaptive Neural Computation

Kim, Byoung-Ho;

Kim, Byoung-Ho;

Abstract

This paper presents an adaptive neural computation algorithm for multi-layered neural networks which are applied to identify the characteristic function of dynamic systems. The main feature of the proposed algorithm is that the initial learning rate for the employed neural network is assigned systematically, and also the assigned learning rate can be adjusted empirically for effective neural leaning. By employing the approach, enhanced modeling of dynamic systems is possible. The effectiveness of this approach is veri tied by simulations.

Keywords

dynamic system modeling;adaptive neural computation;dynamic learning rate;

Language

Korean

Cited by

References

1.

K. Hirai, M. Hirose, Y. Haikawa, and T. Takenaka, 'The development of honda humanoid robot,' Proc. of IEEE Int. Conf. on Robotics and Automation, pp. 1321-1326, 1998

2.

K. Akachi, K. Kaneko, N. Kanehira, S. Ota, G. Miyamori, M. Hirata, S. Kajita, and F. Kanehiro, 'Development of humanoid robot HRP-3P,' Proc. of 2005 5th IEEE-RAS lnternational Conference on Humanoid Robots, pp. 50-55,2005

3.

Y. Xia, J. Wang, and L.-M. Fok, 'Grasping-force optimization for multifingered robotic hands using a recurrent neural network,' IEEE Transactions on Robotics and Automation, 20(3), pp. 549-554, 2004

4.

A. C. Smith, F. Mobasser, and K. H-Zaad, 'Neural-network-based contact force observers for haptic applications,' IEEE Transactions on Robotics, 22(6), pp.1163-1175, 2006

5.

D. E. Rumelhalt, G. E. Hinton, and R. J. Williams, Learning Internal Representations by Error Propagation, Parallel Distributed Processing, vol. 1, MIT press, pp. 318-362, 1986

6.

D. Psaltis, A. Sideris, and A. Yamamura, 'A multilayer neural network controller,' IEEE Control System Magazine, pp. 17-21, April 1988

7.

K. S. Narendra and K. Parthasarathy, 'Identification and control of dynamical systems using neural networks,' IEEF Transaction on Neural Networks, 1, pp. 4-27, 1990

8.

T. Ozaki, T. Suzuki, T. Furuhashi, S. Okuma, and Y. Uchikawa, 'Trajectory control of robotic manipulators using neural networks,' IEEE Trans. On Industrial Electronics, 38(3), pp. 195-202, 1991

9.

J. G Kuschewski, S. Hui, and S. H. Zak, 'Application of feedforward neural network to dynamical system identification and control,' IEEE Trans. on Control Systems Technology, 1 ( 1 ), pp.37-49, 1993

10.

M. D. Lemmon and A. N. Michel, 'Neural networks in control, identification, and decision making,' IEEE Transaction on Automatic Control, 44, pp. 1993-1994, 1999

11.

H. C. Hsin, C. C. Li, and R. J. Sclabassi, 'An adaptive training algorithm for back-propagation neural networks,' IEEE Trans. on Systems, Man, and Cybernetics, pp. 512-514, March 1995

12.

L. Jin, P. N. Nikiforuk, and M. M. Gupta, 'Fast neural learning and control of discrete-time nonlinear systems,' IEEE Trans. on Systems, Man, and Cybernetics, pp. 478-488, March 1995

13.

R. H. Nielsen, Neurocomputing. Ch. 6, Addison-Wesley, pp. 183-191, 1990