Adaptive control scheme based on the least squares support vector machine network

Bezdek, J. (1981). Pattern Recognition with Fuzzy Objective Function-Algorithms, Plenum Press, New York, NY.10.1007/978-1-4757-0450-1Search in Google Scholar

Faanes, A. and Skogestad, S. (2004). pH-neutralization: Integrated process and control design, Computers and Chemical Engineering 28(8): 1475-1487.10.1016/j.compchemeng.2003.11.001Search in Google Scholar

Ge, S. S. and Wang, C. (2004). Adaptive neural control of uncertain MIMO nonlinear systems, IEEE Transactions on Neural Networks 15(3): 674-692.10.1109/TNN.2004.826130Search in Google Scholar

Ge, S. S., Yang, C. and Lee, T. H. (2008). Adaptive predictive control using neural network for a class of pure-feedback systems in discrete time, IEEE Transactions on Neural Networks 19(9): 1599-1614.10.1109/TNN.2008.2000446Search in Google Scholar

Henson, M. A. and Seborg, D. E. (1994). Adaptive nonlinear control of a pH neutralization process, IEEE Transactions on Control System Technology 2(3): 169-182.10.1109/87.317975Search in Google Scholar

Huicheng, W. L. L. and Taiyi, Z. (2008). An improved algorithm on least squares support vector machines, Information Technology Journal 7(2): 370-373.10.3923/itj.2008.370.373Search in Google Scholar

Ku, C.-C., and Lee, K. Y. (1995). Diagonal recurrent neural networks for dynamic systems control, IEEE Transactions on Neural Networks 6(1): 144-156.10.1109/72.363441Search in Google Scholar

Li-Juan, L., Hong-Y, S. and Jian, C. (2007). Generalized predictive control with online least squares support machines, Acta Automatica Sinica 33(11): pp. 1182-1188.Search in Google Scholar

Li, X., Yi Cao, G. and Jian Zhu, X. (2006). Modeling and control of PEMFC based on least squares support vector machines, Energy Conversion and Management 47(7-8): 1032-1050.10.1016/j.enconman.2005.04.002Search in Google Scholar

Mahmoud, T. A. (2010). Multi resolution wavelet least squares support vector machine network for nonlinear system modeling, Proceedings of the 15th International Conference in Automation and Robotics MMAR2010, Międzyzdroje, Poland, pp. 405-410.Search in Google Scholar

Nie, J., Loh, A. and Hang, C. (1996). Modeling pH neutralization processes using fuzzy-neural approaches, Fuzzy Sets and Systems 78(1): 5-22.10.1016/0165-0114(95)00118-2Search in Google Scholar

Parisini, T. and Zoppoli, R. (1994). Neural networks for feedback feedforward nonlinear control systems, IEEE Transactions on Neural Networks 5(3): 437-449.10.1109/72.28691418267810Search in Google Scholar

Robert, M. S. and Jean-Jacques, E. S. (1992). Gaussian networks for direct adaptive control, IEEE Transactions on Neural Networks 3(6): 837-863.10.1109/72.165588Search in Google Scholar

Saunders, C., Gammerman, A. and Vovk, V. (1998). Ridge regression learning algorithm in dual variables, Proceedings of the 15th International Conference on Machine Learning ICML-98, Madison, WI, USA, pp. 515-521.Search in Google Scholar

Suykens, J. A. K. (2001). Nonlinear modeling and support vector machines, IEEE Instrumentation and Measurement Technology Conference, Budapest, Hungary, pp. 287-294.Search in Google Scholar

Suykens, J. A. K., Van Gestel, T., De Brabanter, J., De Moor, B. and Vandewalle, J. (2002). Least Squares Support Vector Machines, World Scientific, Singapore.10.1142/5089Search in Google Scholar

Suykens, J. A. K. and Vandewalle, J. (1999). Least squares support vector machine classifiers, Neural Processing Letters 9(3): 293-300.10.1023/A:1018628609742Search in Google Scholar

Suykens, J. A. K., Vandewalle, J. and Moor, B. D. (2001). Optimal control by least squares support vector machines, Neural Networks 14: 23-35.10.1016/S0893-6080(00)00077-0Search in Google Scholar

Vapnik, V. (1998). Statistical Learning Theory, JohnWiley, New York, NY.Search in Google Scholar

Wang, Y.-N. and Yuan, X.-F. (2008). SVM approximate-based internal model control strategy, Acta Automatica Sinica 34(2): 696-702.10.3724/SP.J.1004.2008.00172Search in Google Scholar

Wang, Y., Rong, G. and Wang, S. (2002). Hybrid fuzzy modeling of chemical processes, Fuzzy Sets and Systems 130 (2-1): 265-275.10.1016/S0165-0114(01)00242-1Search in Google Scholar

Yoo, S. J., Park, J. B., and Choi, Y. H. (2005). Stable predictive control of chaotic systems using self-recurrent wavelet neural networks, International Journal of Control, Automation, and Systems 3(1): 43-55.Search in Google Scholar

Zhang, L., Zhou, W. and Jiao, L. (2004). Wavelet support vector machine, IEEE Transactions on Systems Man, and Cybernetics 34(1): 34-39.10.1109/TSMCB.2003.811113Search in Google Scholar

Zhang, R. and Wang, S. (2008). Support vector machine based predictive functional control design for output temperature of coking furnace, Journal of Process Control 18(5): 439-448.10.1016/j.jprocont.2007.10.008Search in Google Scholar

Zhang, X.-G., Gao, D., Zhang, X.-G. and Ren, S. S. (2005). Robust wavelet support vector machine for regression estimation, International Journal of Information Technology 11(9): 35-45.Search in Google Scholar