RECURSIVE-IDENTIFICATION USING FEEDFORWARD NEURAL NETWORKS

The paper is concerned with the identification of an unknown nonlinear dynamical system when only the inputs and outputs are accessible for measurement. Under certain assumptions it is shown that, generically, the system can be realized by a recursive input-output model. Furtherm ore, relying on the approximation properties of neural networks and th e existence of effective training algorithms, it is demonstrated how a n effective identification model can be constructed. Simulation result s are presented to complement the theoretical discussions.