Applications of Prediction and Identification Using Adaptive DCMAC Neural Networks
An adaptive dynamic cerebellar model articulation
controller (DCMAC) neural network used for solving the prediction
and identification problem is proposed in this paper. The proposed
DCMAC has superior capability to the conventional cerebellar model
articulation controller (CMAC) neural network in efficient learning
mechanism, guaranteed system stability and dynamic response. The
recurrent network is embedded in the DCMAC by adding feedback
connections in the association memory space so that the DCMAC
captures the dynamic response, where the feedback units act as
memory elements. The dynamic gradient descent method is adopted to
adjust DCMAC parameters on-line. Moreover, the analytical method
based on a Lyapunov function is proposed to determine the
learning-rates of DCMAC so that the variable optimal learning-rates
are derived to achieve most rapid convergence of identifying error.
Finally, the adaptive DCMAC is applied in two computer simulations.
Simulation results show that accurate identifying response and
superior dynamic performance can be obtained because of the
powerful on-line learning capability of the proposed DCMAC.
[1] A. Agarwal, "A systematic classification of neural-network-based
control," IEEE Contr. Syst. Mag., vol. 17, pp. 75-93, 1997.
[2] J. G. Kuschewski, S. Hui, and S. H. Zak, "Application of feed-forward
neural networks to dynamical system identification and control," IEEE
Trans. Contr. Syst., vol. 1, no. 1, pp. 37-49, 1993.
[3] C. M. Lin and C. F. Hsu, "Neural-network-based adaptive control for
induction servomotor drive system", IEEE Trans. Ind. Electron., vol. 49,
no. 1, pp. 115-123, 2002.
[4] C. C. Ku and K. Y. Lee, "Diagonal recurrent neural networks for dynamic
systems control," IEEE Trans. Neural Network, vol. 6, no. 1, pp. 144-156,
1995.
[5] T. W. S. Chow, and Y. Fang, "A recurrent neural-network-based
real-time learning control strategy applying to nonlinear systems with
unknown dynamics," IEEE Trans. Ind. Electron., vol. 45, no. 1, pp.
151-161, 1998.
[6] J. S. Albus, "A new approach to manipulator control: The cerebellar
model articulation controller (CMAC)," J. Dyn. Syst., Measurement,
Contr., vol. 97, no. 3, pp. 220-227, 1975.
[7] S. H. Lane, D. A. Handelman, and J. J. Gelfand, "Theory and
development of higher-order CMAC neural networks," IEEE Contr. Syst.
Mag., vol. 12, no. 2, pp. 23-30, 1992.
[8] K. S. Hwang, and C. S. Lin, "Smooth trajectory tracking of three-link
robot: a self-organizing CMAC approach," IEEE Trans. Syst., Man, and
Cybern., pt. B, vol. 28, no. 5, pp. 680-692, 1998.
[9] F. J. Gonzalez-Serrano, A. R. Figueiras-Vidal, and A. Artes-Rodriguez,
"Generalizing CMAC architecture and training," IEEE Trans. Neural
Networks, vol. 9, no. 6, pp. 1509-1514, 1998.
[10] J. C. Jan and S. L. Hung, "High-order MS_CMAC neural network," IEEE
Trans. Neural Networks, vol. 12, no. 3, pp. 598-603, 2001.
[11] C. T. Chiang and C. S. Lin, "CMAC with general basis functions," Neural
Networks, vol. 9, no. 7, pp. 1199-1211, 1996.
[12] Y. H. Kim, and F. L. Lewis, "Optimal design of CMAC neural-network
controller for robot manipulators," IEEE Trans. Syst., Man, Cybern., pt.
C, vol. 30, no. 1, pp. 22-31, 2000.
[13] S. Jagannathan, "Discrete-time CMAC NN control of feedback
linearizable nonlinear systems under a persistence of excitation," IEEE
Trans. Neural Networks, vol. 10, no. 1, pp. 128-137, 1999.
[14] C.J. Lin and C.C. Chin, "Prediction and identification using
wavelet-based recurrent fuzzy neural networks," IEEE Trans. Syst., Man,
Cybern. B, Cybern., vol. 34, pp. 2144-2154, 2004.
[15] C. H. Lee and C. C. Teng, "Identification and control of dynamic systems
using recurrent fuzzy neural networks," IEEE Trans. Fuzzy Syst., vol. 8,
pp. 349-366, 2000.
[16] K. S. Narendra and K. Parthasarathy, "Identification and control of
dynamical systems using neural networks," IEEE Trans. Neural
Networks, vol. 1, pp. 4-27, Mar. 1990.
[1] A. Agarwal, "A systematic classification of neural-network-based
control," IEEE Contr. Syst. Mag., vol. 17, pp. 75-93, 1997.
[2] J. G. Kuschewski, S. Hui, and S. H. Zak, "Application of feed-forward
neural networks to dynamical system identification and control," IEEE
Trans. Contr. Syst., vol. 1, no. 1, pp. 37-49, 1993.
[3] C. M. Lin and C. F. Hsu, "Neural-network-based adaptive control for
induction servomotor drive system", IEEE Trans. Ind. Electron., vol. 49,
no. 1, pp. 115-123, 2002.
[4] C. C. Ku and K. Y. Lee, "Diagonal recurrent neural networks for dynamic
systems control," IEEE Trans. Neural Network, vol. 6, no. 1, pp. 144-156,
1995.
[5] T. W. S. Chow, and Y. Fang, "A recurrent neural-network-based
real-time learning control strategy applying to nonlinear systems with
unknown dynamics," IEEE Trans. Ind. Electron., vol. 45, no. 1, pp.
151-161, 1998.
[6] J. S. Albus, "A new approach to manipulator control: The cerebellar
model articulation controller (CMAC)," J. Dyn. Syst., Measurement,
Contr., vol. 97, no. 3, pp. 220-227, 1975.
[7] S. H. Lane, D. A. Handelman, and J. J. Gelfand, "Theory and
development of higher-order CMAC neural networks," IEEE Contr. Syst.
Mag., vol. 12, no. 2, pp. 23-30, 1992.
[8] K. S. Hwang, and C. S. Lin, "Smooth trajectory tracking of three-link
robot: a self-organizing CMAC approach," IEEE Trans. Syst., Man, and
Cybern., pt. B, vol. 28, no. 5, pp. 680-692, 1998.
[9] F. J. Gonzalez-Serrano, A. R. Figueiras-Vidal, and A. Artes-Rodriguez,
"Generalizing CMAC architecture and training," IEEE Trans. Neural
Networks, vol. 9, no. 6, pp. 1509-1514, 1998.
[10] J. C. Jan and S. L. Hung, "High-order MS_CMAC neural network," IEEE
Trans. Neural Networks, vol. 12, no. 3, pp. 598-603, 2001.
[11] C. T. Chiang and C. S. Lin, "CMAC with general basis functions," Neural
Networks, vol. 9, no. 7, pp. 1199-1211, 1996.
[12] Y. H. Kim, and F. L. Lewis, "Optimal design of CMAC neural-network
controller for robot manipulators," IEEE Trans. Syst., Man, Cybern., pt.
C, vol. 30, no. 1, pp. 22-31, 2000.
[13] S. Jagannathan, "Discrete-time CMAC NN control of feedback
linearizable nonlinear systems under a persistence of excitation," IEEE
Trans. Neural Networks, vol. 10, no. 1, pp. 128-137, 1999.
[14] C.J. Lin and C.C. Chin, "Prediction and identification using
wavelet-based recurrent fuzzy neural networks," IEEE Trans. Syst., Man,
Cybern. B, Cybern., vol. 34, pp. 2144-2154, 2004.
[15] C. H. Lee and C. C. Teng, "Identification and control of dynamic systems
using recurrent fuzzy neural networks," IEEE Trans. Fuzzy Syst., vol. 8,
pp. 349-366, 2000.
[16] K. S. Narendra and K. Parthasarathy, "Identification and control of
dynamical systems using neural networks," IEEE Trans. Neural
Networks, vol. 1, pp. 4-27, Mar. 1990.
@article{"International Journal of Information, Control and Computer Sciences:64854", author = "Yu-Lin Liao and Ya-Fu Peng", title = "Applications of Prediction and Identification Using Adaptive DCMAC Neural Networks", abstract = "An adaptive dynamic cerebellar model articulation
controller (DCMAC) neural network used for solving the prediction
and identification problem is proposed in this paper. The proposed
DCMAC has superior capability to the conventional cerebellar model
articulation controller (CMAC) neural network in efficient learning
mechanism, guaranteed system stability and dynamic response. The
recurrent network is embedded in the DCMAC by adding feedback
connections in the association memory space so that the DCMAC
captures the dynamic response, where the feedback units act as
memory elements. The dynamic gradient descent method is adopted to
adjust DCMAC parameters on-line. Moreover, the analytical method
based on a Lyapunov function is proposed to determine the
learning-rates of DCMAC so that the variable optimal learning-rates
are derived to achieve most rapid convergence of identifying error.
Finally, the adaptive DCMAC is applied in two computer simulations.
Simulation results show that accurate identifying response and
superior dynamic performance can be obtained because of the
powerful on-line learning capability of the proposed DCMAC.", keywords = "adaptive, cerebellar model articulation controller,CMAC, prediction, identification", volume = "5", number = "6", pages = "701-6", }