A novel dynamic neural network structure for nonlinear system identification
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 16th IFAC World Congress, 2005 |
| Publisher | International Federation of Automatic Control (IFAC) |
| Pages | 1135 |
| ISBN (Print) | 9783902661753 |
| DOIs | |
| Publication status | Published - 2005 |
Publication series
| Name | World congress |
|---|---|
| Publisher | IFAC |
| Number | 1 |
| Volume | 16 |
Related information
ID: 3262580
