Support vector machines based generalized predictive control of chaotic systems

IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences

Volumn E89-A, Issue 10, 2006, Pages 2787-2794

  Iplikci, Serdar ^a  

^a PAMUKKALE UNIVERSITY   (Turkey)

Author keywords

Chaos control; Generalized predictive control; Modeling and prediction; Support vector machines

Indexed keywords

CHAOS THEORY; GAUSSIAN NOISE (ELECTRONIC); MATHEMATICAL MODELS; PARAMETER ESTIMATION; PERTURBATION TECHNIQUES; VECTORS;

CHAOS CONTROL; GENERALIZED PREDICTIVE CONTROL (GPC); MODELING AND PREDICTION; SUPPORT VECTOR MACHINES (SVM);

PREDICTIVE CONTROL SYSTEMS;

EID: 33750037347     PISSN: 09168508     EISSN: 17451337     Source Type: Journal    
DOI: 10.1093/ietfec/e89-a.10.2787     Document Type: Conference Paper

References (38)

1. S. Iplikci, "Support vector machines based generalized predictive control," To be published in International Journal of Robust and Nonlinear Control.
2. E. Ott, C. Grebogi, and J.A. Yorke, "Controlling chaos," Phys. Rev. Lett., vol.64, pp.1196-1199, 1990.
3. F. Romeiras, C. Grebogi, E. Ott, and W.P. Dayawansa, " Controllingchaotic dynamical systems," Physica D, vol.58, pp.165-192, 1992.
4. E. Barreto and C. Grebogi, "Multiparameter control of chaos," Phys. Rev. E, vol.52, pp.3553-3557, 1995.
5. D.W. Clarke, C. Mohtadi, and P.C. Tuffs, "Generalized predictive control -Part 1 : The basic algorithm," Automatica, vol.23, pp. 137-148, 1987.
6. D.W. Clarke, C. Mohtadi, and P.C. Tuffs, "Generalized predictive control -Part 2: The basic algorithm," Automatica, vol.23, pp. 149-163, 1987.
7. D.W. Clarke, Advances in Model-Based Predictive Control, Oxford University Press, 1994.
8. D.W. Clarke and C. Mohtadi, "Properties of generalized predictive control," Automatica, vol.25, pp.859-875, 1989.
9. E.E Camacho, "Constrained generalized predictive control," IEEE Trans. Autom. Control, vol.38, no.2, pp.327-332, 1993.
10. J. Richalet, "Industrial applications of model-based predictive control," Automatica, vol.29, pp.1251-1274, 1993.
11. S.J. Qin and T.A. Badgwell, "A survey of industrial model predictive control technology," Control Engineering Practice, vol.11, pp.733-764, 2003.
12. D. Soloway and P.J. Haley, "Neural generalized predictive control: A Newton-Raphson algorithm," Proc. IEEE International Symposium on Intelligent Control, pp.277-282, Dearborn, MI, 1996.
13. M.T. Hagan, H.B. Demuth, and O. De Jesus, "An introduction to the use of neural networks in control systems," Int. J. Robust Nonlinear Control, vol.12, pp.959-985, 2002.
14. J.M. Zamarreño and P. Vega, "Neural predictive control: Application to a highly non-linear system," Eng. Appl. Artif. Intell., vol.12, pp.149-158, 1999.
15. P.H. Sørensen, M. Nørgaard, O. Ravn, and N.K. Poulsen, "Implementation of neural network based nonlinear predictive control," Neurocomputing, vol.28, pp.37-51, 1999.
16. S.J. Yoo, J.B. Park, and Y.H. Choi, "Stable predictive control of chaotic systems using self-recurrent wavelet neural network," Int. J. Control Autom. Syst., vol.3, pp.43-55, 2005.
17. P. Potočnik and I. Grabec, "Nonlinear model predictive control of a cutting process," Neurocomputing, vol.43, pp.107-126, 2002.
18. Y.L. Huang, H.H. Lou, J.P. Gong, and T.F. Edgar, "Fuzzy model predictive control," IEEE Trans. Fuzzy Syst., vol.8, no.6, pp.665-678, 2000.
19. N. Li, S.Y. Li, and Y.G. Xi, "Multi-model predictive control based on the Takagi-Sugeno fuzzy models: A case study," Inf. Sci., vol.165, pp.247-263, 2004.
20. J. Espinosa and J. Vandewalle, "Predictive control using fuzzy models," in Advances in Soft Computing Engineering Design and Manufacturing, ed. R. Roy, T. Furuhashi, and Chawdhry, Springer Verlag, London, 1999.
21. L. Chen and G. Chen, "Fuzzy modeling, prediction, and control of uncertain chaotic systems based on time series," IEEE Trans. Circuits Syst. I, Fundam. Theory Appl., vol.47, no. 10, pp. 1527-1531, 2000.
22. L. Chen and G. Chen, "Fuzzy predictive control of uncertain chaotic systems using time series," Int. J. Bifurcation Chaos, vol.9, pp.757-767, 1999.
23. J.Q. Hu and E. Rose, "Generalized predictive control using a neurofuzzy model," Int. J. Syst. Sci., vol.30, pp.117-122, 1999.
24. H. Sarimveis and G. Bafas, "Fuzzy model predictive control of non-linear processes using genetic algorithms," Fuzzy Sets Syst., vol. 139, pp.59-80, 2003.
25. J.T. Choi and Y.H. Choi, "Fuzzy neural network based predictive control of chaotic nonlinear systems," IEICE Trans. Fundamentals, vol.E87-A, no.5, pp.1270-1279, May 2004.
26. M. Martinez, J.S. Senent, and X. Blasco, "Generalized predictive control using genetic algorithms (GAGPC)," Eng. Appl. Artif. Intell., vol.11, pp.355-367, 1998.
27. V. Vapnik, The Nature of Statistical Learning Theory, Springer-Verlag, 1995.
28. V. Vapnik, Statistical Learning Theory, John Wiley, New York, 1998.
29. V. Vapnik, "The support vector method of function estimation," in Nonlinear Modeling Advanced Black Box Techniques, pp.55-85, ed. J.A.K. Suykens and J. Vandewalle, Kluwer Academic Pub., 1998.
30. N. Cristianini and J.S. Taylor, An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods, Cambridge University Press, New York, 2000.
31. B. Schölkopf, C.J.C. Burges, and A.J. Smola, Advances in Kernel Methods: Support Vector Learning, MIT Press, Cambridge, MA, 1999.
32. J.A.K. Suykens and J. Vandewalle, "Chaos control using least-squares support vector machines," Int. J. Circuit Theory Appl., vol.27, pp.605-615, 1999.
33. A. Kulkarni, V.K. Jayaraman, and B.D. Kulkarni, "Control of chaotic dynamical systems using support vector machines," Phys. Lett. A, vol.317, pp.429-435, 2003.
34. A.J. Smola and B. Schölkopf, "A tutorial on support vector regression," NeuroCOLT Tech. Rep. No. NC-TR-98-030, Royal Holloway College, University of London, 1998.
35. V. Cherkassky and Y. Ma, "Practical selection of SVM parameters and noise estimation for SVM regression," Neural Netw., vol.17, pp. 113-126, 2004.
36. J. Nocedal and S.J. Wright, Numerical Optimization, Springer-Verlag, New York, 1999.
37. P. Venkataraman, Applied Optimization with MatLab Programming, John Wiley and Sons, New York, 2002.
38. R. Šindelář and R. Babuška, "Input selection for nonlinear regression models," IEEE Trans. Fuzzy Syst., vol.12, no.5, pp.688-696, 2004.