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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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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creator Cai, Lin
Huang, Yuancan
Lu, Shaolin
Chen, Jiabin
description 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.
doi_str_mv 10.1109/ICCA.2007.4376518
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subjects Artificial neural networks
Clustering algorithms
Fault diagnosis
Filtering algorithms
Kalman filters
Neural networks
Position measurement
Satellites
Sensor systems
Vectors
title Using RBF Neural Network for Fault Diagnosis in Satellite ADS
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