摘要
In this paper, a new hybrid learning strategy composed of K-means clustering algorithm and Kalman filtering Is employed to train radial based function (RBF) neural network for fault diagnosis In satellite attitude determination system. Because Kalman filtering and K-means clustering algorithm both adopt linear update rule, their combination produces a new hybrid training algorithm that can converges quickly, Simulation results demonstrate that the proposed approach is effective for fault diagnosis In satellite attitude determination system.
| 源语言 | 英语 |
|---|---|
| 主期刊名 | 2007 IEEE International Conference on Control and Automation, ICCA |
| 出版商 | Institute of Electrical and Electronics Engineers Inc. |
| 页 | 1052-1055 |
| 页数 | 4 |
| ISBN（印刷版） | 1424408180, 9781424408184 |
| DOI | |
| 出版状态 | 已出版 - 2007 |
| 活动 | 2007 IEEE International Conference on Control and Automation, ICCA - Guangzhou, 中国 期限: 30 5月 2007 → 1 6月 2007 |
出版系列
| 姓名 | 2007 IEEE International Conference on Control and Automation, ICCA |
|---|
会议
| 会议 | 2007 IEEE International Conference on Control and Automation, ICCA |
|---|---|
| 国家/地区 | 中国 |
| 市 | Guangzhou |
| 时期 | 30/05/07 → 1/06/07 |
指纹
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Cai, L., Huang, Y., Lu, S., & Chen, J. (2007). Using RBF neural network for fault diagnosis in satellite ADS. 在 2007 IEEE International Conference on Control and Automation, ICCA (页码 1052-1055). 文章 4376518 (2007 IEEE International Conference on Control and Automation, ICCA). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCA.2007.4376518