Abstract
A new designing method of a robust neural network controller against system environment changes using Universal Learning Network (ULN) is considered in this paper. With the introduced method, the worst values of system parameters can be searched as well as the optimization of controller parameters through a dual learning algorithm, which includes maximization and minimization procedures. Therefore, the robust controller can be obtained by minimizing the criterion function regarding the worst values of system parameters. Simulation results demonstrate that the system performance has been improved compared with the conventional method by using the proposed method.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the International Joint Conference on Neural Networks |
| Pages | 497-502 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 2001 |
| Externally published | Yes |
| Event | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC Duration: 2001 Jul 15 → 2001 Jul 19 |
Other
| Other | International Joint Conference on Neural Networks (IJCNN'01) |
|---|---|
| City | Washington, DC |
| Period | 01/7/15 → 01/7/19 |
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ASJC Scopus subject areas
- Software
Cite this
A new robust neural network controller designing method for nonlinear systems. / Chen, H.; Hirasawa, K.; Furuzuki, Takayuki; Murata, J.
Proceedings of the International Joint Conference on Neural Networks. Vol. 1 2001. p. 497-502.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - A new robust neural network controller designing method for nonlinear systems
AU - Chen, H.
AU - Hirasawa, K.
AU - Furuzuki, Takayuki
AU - Murata, J.
PY - 2001
Y1 - 2001
N2 - A new designing method of a robust neural network controller against system environment changes using Universal Learning Network (ULN) is considered in this paper. With the introduced method, the worst values of system parameters can be searched as well as the optimization of controller parameters through a dual learning algorithm, which includes maximization and minimization procedures. Therefore, the robust controller can be obtained by minimizing the criterion function regarding the worst values of system parameters. Simulation results demonstrate that the system performance has been improved compared with the conventional method by using the proposed method.
AB - A new designing method of a robust neural network controller against system environment changes using Universal Learning Network (ULN) is considered in this paper. With the introduced method, the worst values of system parameters can be searched as well as the optimization of controller parameters through a dual learning algorithm, which includes maximization and minimization procedures. Therefore, the robust controller can be obtained by minimizing the criterion function regarding the worst values of system parameters. Simulation results demonstrate that the system performance has been improved compared with the conventional method by using the proposed method.
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UR - http://www.scopus.com/inward/citedby.url?scp=0034850560&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:0034850560
VL - 1
SP - 497
EP - 502
BT - Proceedings of the International Joint Conference on Neural Networks
ER -