Using RBF neural network for fault diagnosis in satellite ADS

Lin Cai; Yuancan Huang; Shaolin Lu; Jiabin Chen

doi:10.1109/ICCA.2007.4376518

Using RBF neural network for fault diagnosis in satellite ADS

Lin Cai^*, Yuancan Huang, Shaolin Lu, Jiabin Chen

^*Corresponding author for this work

Beijing Institute of Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

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.

Original language	English
Title of host publication	2007 IEEE International Conference on Control and Automation, ICCA
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1052-1055
Number of pages	4
ISBN (Print)	1424408180, 9781424408184
DOIs	https://doi.org/10.1109/ICCA.2007.4376518
Publication status	Published - 2007
Event	2007 IEEE International Conference on Control and Automation, ICCA - Guangzhou, China Duration: 30 May 2007 → 1 Jun 2007

Publication series

Name	2007 IEEE International Conference on Control and Automation, ICCA

Conference

Conference	2007 IEEE International Conference on Control and Automation, ICCA
Country/Territory	China
City	Guangzhou
Period	30/05/07 → 1/06/07

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10.1109/ICCA.2007.4376518

Cite this

Cai, L., Huang, Y., Lu, S., & Chen, J. (2007). Using RBF neural network for fault diagnosis in satellite ADS. In 2007 IEEE International Conference on Control and Automation, ICCA (pp. 1052-1055). Article 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

@inproceedings{7a520a28bc5f47a18625e1ee950fd79d,

title = "Using RBF neural network for fault diagnosis in satellite ADS",

abstract = "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.",

author = "Lin Cai and Yuancan Huang and Shaolin Lu and Jiabin Chen",

year = "2007",

doi = "10.1109/ICCA.2007.4376518",

language = "English",

isbn = "1424408180",

series = "2007 IEEE International Conference on Control and Automation, ICCA",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1052--1055",

booktitle = "2007 IEEE International Conference on Control and Automation, ICCA",

address = "United States",

note = "2007 IEEE International Conference on Control and Automation, ICCA ; Conference date: 30-05-2007 Through 01-06-2007",

}

Cai, L, Huang, Y, Lu, S & Chen, J 2007, Using RBF neural network for fault diagnosis in satellite ADS. in 2007 IEEE International Conference on Control and Automation, ICCA., 4376518, 2007 IEEE International Conference on Control and Automation, ICCA, Institute of Electrical and Electronics Engineers Inc., pp. 1052-1055, 2007 IEEE International Conference on Control and Automation, ICCA, Guangzhou, China, 30/05/07. https://doi.org/10.1109/ICCA.2007.4376518

Using RBF neural network for fault diagnosis in satellite ADS. / Cai, Lin; Huang, Yuancan; Lu, Shaolin et al.
2007 IEEE International Conference on Control and Automation, ICCA. Institute of Electrical and Electronics Engineers Inc., 2007. p. 1052-1055 4376518 (2007 IEEE International Conference on Control and Automation, ICCA).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using RBF neural network for fault diagnosis in satellite ADS

AU - Cai, Lin

AU - Huang, Yuancan

AU - Lu, Shaolin

AU - Chen, Jiabin

PY - 2007

Y1 - 2007

N2 - 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.

AB - 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.

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U2 - 10.1109/ICCA.2007.4376518

DO - 10.1109/ICCA.2007.4376518

M3 - Conference contribution

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SN - 1424408180

SN - 9781424408184

T3 - 2007 IEEE International Conference on Control and Automation, ICCA

SP - 1052

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BT - 2007 IEEE International Conference on Control and Automation, ICCA

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2007 IEEE International Conference on Control and Automation, ICCA

Y2 - 30 May 2007 through 1 June 2007

ER -

Using RBF neural network for fault diagnosis in satellite ADS

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