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.
Keywords
- Support Vector Machine
- Little Square Support Vector Machine
- Hypothesis Space
- Standard Support Vector Machine
- Little Square Support Vector Machine Model
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2005 Springer-Verlag Berlin Heidelberg
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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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