抄録
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.
| 元の言語 | 英語 |
|---|---|
| ページ | 497-502 |
| ページ数 | 6 |
| 出版物ステータス | 出版済み - 1 1 2001 |
| イベント | International Joint Conference on Neural Networks (IJCNN'01) - Washington, DC, 米国 継続期間: 7 15 2001 → 7 19 2001 |
その他
| その他 | International Joint Conference on Neural Networks (IJCNN'01) |
|---|---|
| 国 | 米国 |
| 市 | Washington, DC |
| 期間 | 7/15/01 → 7/19/01 |
Fingerprint
All Science Journal Classification (ASJC) codes
- Software
- Artificial Intelligence
これを引用
A new robust neural network controller designing method for nonlinear systems. / Chen, H.; Hirasawa, K.; Hu, J.; Murata, Junichi.
2001. 497-502 論文発表場所 International Joint Conference on Neural Networks (IJCNN'01), Washington, DC, 米国.研究成果: 会議への寄与タイプ › 論文
}
TY - CONF
T1 - A new robust neural network controller designing method for nonlinear systems
AU - Chen, H.
AU - Hirasawa, K.
AU - Hu, J.
AU - Murata, Junichi
PY - 2001/1/1
Y1 - 2001/1/1
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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M3 - Paper
SP - 497
EP - 502
ER -