Wiener systems which consist of a dynamic linear block followed by a static nonlinear element have been used in numerous applications. In many cases, the system parameters are affected by changes in the environmental conditions. This paper describes a new approach to the on-line identification of time-varying Wiener systems. The time-varying linear parameters and the static nonlinear characteristics are estimated by the neural networks which can represent various nonlinear characteristics. The initial states of the linear model in each estimation window are not available in the Wiener systems. Thus, the initial states and the other system parameters are estimated simultaneously by the nonlinear optimization techniques. Furthermore, the optimal numbers of hidden units in the neural networks are determined by the minimum description length (MDL) criterion.
As the result of the simulation of this method, more accurate parameters can be obtained than the result without the estimation of initial states and MDL.