A universal construction of Artstein's theorem on nonlinear stabilization
Systems & Control Letters
The nature of statistical learning theory
The nature of statistical learning theory
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
Stable adaptive neuro-control design via Lyapunov function derivative estimation
Automatica (Journal of IFAC)
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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 support vector machine adaptive control by Lyapunov function derivative estimation. The support vector machine is trained by particle filter. The support vector machine is applied to estimate the Lyapunov function derivative for affine nonlinear system, whose nonlinearities are assumed to be unknown. In order to demonstrate the availability of this new method of Lyapunov function derivative estimation, we give a simple example in the form of affine nonlinear system. The result of simulation demonstrates that the sequential training algorithm of support vector machine is effective and support vector machine adaptive control can achieve a satisfactory performance.