Abstract
Based on rolling optimization method and on-line learning strategies, a novel weighted least squares support vector machines (WLS-SVM) are proposed for nonlinear system identification in this paper. The good robust property of the novel approach enhances the generalization ability of LS-SVM method, and a real world nonlinear time-variant system is presented to test the feasibility and the potential utility of the proposed method.
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References
Vapnik, V.: The Nature of Statistical Learning Theory, 2nd edn. Springer, New York (1998)
Burges, C.J.C.: A tutorial on support vector machines for pattern recognition. Data Mining Knowl. Disc. 2(2), 1–47 (1998)
Drucker, H., Burges, C.J.C., Kaufman, L.: Support vector regression machines. In: Mozer, M., Jordan, M., Petsche, T. (eds.) Advances in Neural Information Processing Systems, vol. 9, pp. 155–161. MIT Press, Cambridge (1997)
Kecman, V.: Learning and Soft Computing, Support Vector machines, Neural Networks and Fuzzy Logic Models. The MIT Press, Cambridge (2001)
Wang, L.P. (ed.): Support Vector Machines: Theory and Application. Springer, Berlin (2005)
Suykens, J.A.K., Vandewalle, J.: Recurrent least squares support vector machines. IEEE Transactions on Circuits and Systems, part I 47(7), 1109–1114 (2000)
Suykens, J.A.K., Vandewalle, J., De Moor, B.: Optimal control by least squares support vector machines. Neural Networks 14(1), 23–35 (2001)
Suykens, J.A.K.: Support vector machines: a nonlinear modeling and control perspective. European Journal of Control 7(2-3), 311–327 (2001)
Golub, G.H., Van Loan, C.F.: Matrix Computations. Johns Hopkins University Press, Baltimore (1989)
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Wen, X., Xu, X., Cai, Y. (2005). Study of On-line Weighted Least Squares Support Vector Machines. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3610. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539087_7
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DOI: https://doi.org/10.1007/11539087_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28323-2
Online ISBN: 978-3-540-31853-8
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