Identification of Wiener-MLP with feedback NOE-model with extended Kalman filter

The classical input-output Wiener-representation consists of linear dynamics (Laguerre filters) and static nonlinear static polynomial mapping. In Wiener-MLP the static nonlinear mapping is realized with MLP. By feeding back some of the outputs of Wiener-MLP, a model capable of modeling autonomous systems, like batch processes, can be realized. The dynamics contains MLP and Laguerre system in the feedback loop. The model can be presented in state-space form. The MLP can be interpreted as the measurement equation of the system. An extended Kalman filter is used in recursive NOE-type estimation of the MLP parameters in order to identify a model suitable for simulation. Wiener-MLP with feedback models are identified for two bioprocesses