APPLICATION OF SUPPORT VECTOR REGRESSION TO NONLINEAR SYSTEM IDENTIFICATION

This paper applies Support Vector Regression(SVR) to nonlinear system identification problem. Using the basic idea of Gaussian SVR and -insensitive loss function, we propose a new algorithm for nonlinear system identification and compare the Gaussian SVR with the radial basis function(RBF) network for system identification. The performance of the SVR is illustrated by a simulation example involving a benchmark nonlinear system.