A new RBF neural network with GA-based fuzzy C-means clustering algorithm for sins fault diagnosis

Authors:
Zhide Liu;Jiabin Chen;Chunlei Song
Affiliations:
School of Automation, Beijing Institute of Technology, Beijing, China;School of Automation, Beijing Institute of Technology, Beijing, China;School of Automation, Beijing Institute of Technology, Beijing, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese Control and Decision Conference
Year:
2009

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Abstract

In this paper, a new radial basis function (RBF) neural network with fuzzy c-means clustering algorithm based on genetic algorithm (GA) is proposed for the fault diagnosis of gyroscopes and accelerometers of strapdown inertial navigation system (SINS). The fuzzy c-means algorithm (FCM) tends to fall into the local optimum. The fuzzy c-means clustering algorithm combined with GA (FGA) obtains the global optimal cluster centers. FGA is used to provide the optimal cluster centers for RBF neural network, and a second order learning algorithm is used to train the parameters and weights of RBF neural network. Experimental results show that the proposed RBF neural network with FGA quickly converges and effectively improves the diagnostic accuracy rate of SINS fault diagnosis.