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 |
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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 |
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Publisher | IFAC |
Number | 1 |
Volume | 16 |
Related information
ID: 3262580