Name
Abstract | ||
|---|---|---|
Deep recurrent neural networks are used as a means for nonlinear system identification. It is shown that state space models can be transformed into recurrent neural networks and vice versa. This transformation and the understanding of the long short-term memory cell in terms of common system identification nomenclature makes the advances in deep learning more accessible to the controls and system identification communities. A systematic study of deep recurrent neural networks is carried out on a state-of-the-art system identification benchmark. The results indicate that if high amounts of data are available, standard recurrent neural networks achieve comparable performance to state-of-the-art system identification methods. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/SSCI44817.2019.9003133 | 2019 IEEE Symposium Series on Computational Intelligence (SSCI) |
Keywords | Field | DocType |
nonlinear system identification,deep learning,recurrent neural networks | Computer science,Recurrent neural network,Nonlinear system identification,Artificial intelligence,Deep learning,System identification,State space,Memory cell | Conference |
ISBN | Citations | PageRank |
978-1-7281-2486-5 | 0 | 0.34 |
References | Authors | |
4 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Max Schüssler | 1 | 0 | 0.34 |
| Tobias Munker | 2 | 1 | 0.72 |
| Oliver Nelles | 3 | 0 | 0.34 |