Extended Kalman filter trained recurrent radial basis function network in nonlinear system identification

Abstract

We consider the recurrent radial basis function network as a model of nonlinear dynamic system. On-line parameter and structure adaptation is unified under the framework of extended Kalman filter. The ability of adaptive system to deal with high observation noise, and the generalization ability of the resulting RRBF network are demonstrated in nonlinear system identification. © Springer-Verlag Berlin Heidelberg 2002.