Control of Chaotic Dynamical Systems using RBF Networks
This paper presents a novel control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. The proposed method first identifies the nonlinear part of the chaotic system off-line and then constructs a model-following controller using only the estimated system parameters. Simulation results show the effectiveness of the proposed control scheme.
[1] S. Haykin, Neural Networks - A Comprehensive Foundation, Macmillan, New York, 1994. [2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, ''Learning internal representations by error propagation,'' in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, eds., vol. 1, chapter 8, Cambridge, MA: MIT Press, 1986. [3] K. S. Narendra and K. Parthasarathy, ''Identification and control of dynamical systems using neural networks,'' IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4-27, 1990. [4] P. D. Wasserman, Advanced Method in Neural Networks, Van Nostrand Reinhold, New York, pp. 147-176, 1993. [5] D. Sbarbaro, J. P. Segovia, S. Alcozer and J. Gonzales, ''Applications of radial basis network technology to process control,'' IEEE Trans. Control Systems Technology, vol. 8, no. 1, pp. 14-22, 2000. [6] K. B. Kim, J. B. Park, Y. H. Choi and G. Chen, ''Control of chaotic dynamical systems using radial basis function approximators,'' Information Sciences, vol. 130, pp. 165-183, 2000. [7] G. H. Golub and C. F. Van-Loan, Matrix Computation, 3rd Edition, The Johns Hopkins Univ. Press, Baltimore and London, 1996. [8] G. F. Franklin, J. David Powell and Michael L. Workman, Digital Control of Dynamic Systems, Addison-Wesley, New York, 1990.
[1] S. Haykin, Neural Networks - A Comprehensive Foundation, Macmillan, New York, 1994. [2] D. E. Rumelhart, G. E. Hinton and R. J. Williams, ''Learning internal representations by error propagation,'' in Parallel Distributed Processing: Explorations in the Microstructure of Cognition, D. E. Rumelhart and J. L. McClelland, eds., vol. 1, chapter 8, Cambridge, MA: MIT Press, 1986. [3] K. S. Narendra and K. Parthasarathy, ''Identification and control of dynamical systems using neural networks,'' IEEE Trans. Neural Networks, vol. 1, no. 1, pp. 4-27, 1990. [4] P. D. Wasserman, Advanced Method in Neural Networks, Van Nostrand Reinhold, New York, pp. 147-176, 1993. [5] D. Sbarbaro, J. P. Segovia, S. Alcozer and J. Gonzales, ''Applications of radial basis network technology to process control,'' IEEE Trans. Control Systems Technology, vol. 8, no. 1, pp. 14-22, 2000. [6] K. B. Kim, J. B. Park, Y. H. Choi and G. Chen, ''Control of chaotic dynamical systems using radial basis function approximators,'' Information Sciences, vol. 130, pp. 165-183, 2000. [7] G. H. Golub and C. F. Van-Loan, Matrix Computation, 3rd Edition, The Johns Hopkins Univ. Press, Baltimore and London, 1996. [8] G. F. Franklin, J. David Powell and Michael L. Workman, Digital Control of Dynamic Systems, Addison-Wesley, New York, 1990.
@article{"International Journal of Electrical, Electronic and Communication Sciences:54950", author = "Yoichi Ishikawa and Yuichi Masukake and Yoshihisa Ishida", title = "Control of Chaotic Dynamical Systems using RBF Networks ", abstract = "This paper presents a novel control method based on radial basis function networks (RBFNs) for chaotic dynamical systems. The proposed method first identifies the nonlinear part of the chaotic system off-line and then constructs a model-following controller using only the estimated system parameters. Simulation results show the effectiveness of the proposed control scheme. ", keywords = "Chaos, nonlinear plant, radial basis function network.", volume = "1", number = "1", pages = "26-4", }
