Abstract
Support vector machine is a new and promising technique for pattern classification and regression estimation. The training of support vector machine is characterized by a convex optimization problem, which involves the determination of a few additional tuning parameters. Moreover, the model complexity follows from that of this convex optimization problem. In this paper we introduce the sequential support vector machine for the regression estimation. The support vector machine is trained by the Kalman filter and particle filter respectively and then we design a controller based on the sequential support vector machine. Support vector machine controller is designed in the state feedback control of nonaffine nonlinear systems. The results of simulation demonstrate that the sequential training algorithms of support vector machine are effective and sequential support vector machine controller can achieve a satisfactory performance.
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© 2005 Springer-Verlag Berlin Heidelberg
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Sun, Z., Sun, Y., Yang, X., Wang, Y. (2005). Sequential Support Vector Machine Control of Nonlinear Systems by State Feedback. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_2
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DOI: https://doi.org/10.1007/11427469_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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