• 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. ()
  • ISNN (3) ()
  • 1 ()
is ?:cites of
  • 2016-06-24 ()
  • 10.1007/11427469_2 ()
  • 14 ()
  • 1 ()
is ?:hasCitingEntity of
  • en ()
  • 2005-05-30 ()
  • Springer, Berlin, Heidelberg ()
  • 22001 ()
  • 10 ()
  • 7 ()
  • Sequential support vector machine control of nonlinear systems by state feedback ()


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