Using RBF Neural Network for Fault Diagnosis in Satellite ADS

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 a...

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Bibliographic Details
Main Authors: Cai, Lin, Huang, Yuancan, Lu, Shaolin, Chen, Jiabin
Format: Conference Proceeding
Language:English
Subjects:
Artificial neural networks
Clustering algorithms
Fault diagnosis
Filtering algorithms
Kalman filters
Neural networks
Position measurement
Satellites
Sensor systems
Vectors
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Description
Summary: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 converge quickly. Simulation results demonstrate that the proposed approach is effective for fault diagnosis in satellite attitude determination system.
ISSN:1948-3449
1948-3457
DOI:10.1109/ICCA.2007.4376518