A recurrent network for dynamic system identification

This paper presents a type of recurrent artificial neural network architecture for identification of an arbitrary, continuous dynamic system. The recurrent network is shown to be stable for a constant input with certain conditions on the parameters of the network. The proposed network has significant advantages over similar models in continuous time nonlinear system identification and is used to identify three nonlinear dynamic systems. Finally, the applicability of the radial basis function networks using the same network architecture to reduce the time-complexity of the training task is presented.