A SUPPORT VECTOR REGRESSION METHOD FOR REDUCING THE HIGH-ORDER SYSTEMS TO FIRST-ORDER PLUS TIME-DELAY FORMS

Year 2011, Volume: 11 Issue: 1, 1305 - 1309, 28.03.2012

Rana Ortac Kabaoglu

Abstract

In this paper, a novel method for reducing the high-order systems to first-order plus time-delay forms is introduced. For this purpose Support Vector Machines, which became a popular learning algorithm, is employed. Three parameters of the first-order plus time-delay forms are estimated by three parallel support vector regression machines. Satisfactory performance is obtained at the simulations.

Keywords: Support vector regression machines, high-order systems, first-order plus time-delay systems, modeling.

Keywords

Support vector regression machines, high-order systems, first-order plus time-delay systems, modeling

References

• J.P., Richard, “Time Delay Systems: An Overview of Some Recent Advances and Open Problems”, Automatica, Vol: 39, Issue: 10, pp. 1667-1694, 2003.
• S.V., Drakunov, W., Perruquetti, J.P., Richard, “Delay Identification in Time-Delay Systems Using Variable Structure Observers”, Annual Reviews in Control, Vol: 30, Issue: 2, pp. 143-158, 2006.
• V., Venkatashankar, M., Chidambaram, “Design of P and PI Controllers for Unstable 1st Order Plus Time-Delay Systems”, International Journal of Control, Vol: 60, No:1, pp.137-144, 1994.
• S., Tavakoli, M., Tavakoli, “Optimal Tuning of PID Controllers for First Order Plus Time Delay Models Using Dimensional Analysis”, The fourth International Conference on Control and Automation (ICCA’03), Montreal, Canada, 2003.
• S., Skogestad, “Simple Analytic Rules for Model Reduction and PID Controller Tuning”, Journal of Process Control, Vol: 13, pp.291-309, 2003.
• V.N., Vapnik “The Nature of Statistical Learning Theory”, Springer-Verlag, New York. 1995.
• V.N., Vapnik “Statistical Learning Theory”, John Wiley and Sons, New York. 1998.
• N., Cristianini, J., Shawe-Taylor, “An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods”, Cambridge University Press, Cambridge, 2000.
• L, Zhao, J, Sun, KR. Butts, “Linear programming support vector regression with wavelet kernel: A new approach to nonlinear dynamical systems identification”, Mathematics and Computers in Simulation, 2009;79:2051–2063.
• S., Iplikci, “A support vector machine based control application to the experimental three-tank system”, ISA Transactions 2010;49:376-386.
• A.J., Smola, B., Schoelkopf, “A tutorial on support vector regression”, NeuroCOLT2 Technical Report NCTR-98-030, Royal Holloway College, University of London, UK. 1998.
• J., Ma, J., Theiler, S., Perkins, “Accurate on-line support vector regression”, Neural Computation, vol.15, p: 2683-2703, 2003.
• H., Zhang, X., Wang, C., Zhang, X., Xu, “Modeling Nonlinear Dynamical Systems Using Support Vector Machine”, Proceeding of 4. International Conf. On Machine Learning and Cybernetics, p:3204-3209, 2005.