USE OF MULTILAYER FEEDFORWARD NEURAL NETWORKS IN IDENTIFICATION AND CONTROL OF WIENER MODEL

The problem of identification and control of a Wiener model is studied . The proposed identification model uses a hybrid model consisting of a linear autoregressive moving average model in cascade with a multila yer feedforward neural network. A two-step procedure is proposed to es timate the linear and nonlinear parts separately. Control of the Wiene r model can be achieved by inserting the inverse of the static nonline arity in the appropriate loop locations. Simulation results illustrate the performance of the proposed method.