Sequential support vector machine control of nonlinear systems by state feedback

Authors:
Zonghai Sun;Youxian Sun;Xuhua Yang;Yongqiang Wang
Affiliations:
National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China;National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China;College of Information Engineering, Zhejiang University of Technology, Hangzhou, China;National Laboratory of Industrial Control Technology, Zhejiang University, Hangzhou, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

Citing 7
Cited 0

Sequential Monte Carlo Methods to Train Neural Network Models

Neural Computation
Rao-blackwellised particle filtering for dynamic Bayesian networks

UAI'00 Proceedings of the Sixteenth conference on Uncertainty in artificial intelligence
A survey of convergence results on particle filtering methods forpractitioners

IEEE Transactions on Signal Processing
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking

IEEE Transactions on Signal Processing
Particle filters for state-space models with the presence ofunknown static parameters

IEEE Transactions on Signal Processing
Adaptive neural network control of nonlinear systems by state andoutput feedback

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Adaptive output feedback control of nonlinear systems using neural networks

Automatica (Journal of IFAC)

Quantified Score

Hi-index	0.00

Visualization

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.