Dynamic neural networks are often used for nonlinear system identification. This paper presents a novel series-parallel dynamic neural network structure which is suitable for nonlinear system identification. A theoretical proof is given showing that this type of dynamic neural network is able to approximate finite trajectories of nonlinear dynamical systems. Also, this neural network is trained to identify a practical nonlinear 3D crane system.
|Title of host publication||Proceedings of the 16th IFAC World Congress, 2005|
|Publisher||International Federation of Automatic Control (IFAC)|
|Publication status||Published - 2005|