Identification of the acoustic fault sources of underwater vehicles based on modular structure variable RBF network

Authors:
Linke Zhang;Lin He;Kerong Ben;Na Wei;Yunfu Pang;Shijian Zhu
Affiliations:
Institute of Noise and Vibration, Navy University of Engineering, Wuhan, Hubei, China;Institute of Noise and Vibration, Navy University of Engineering, Wuhan, Hubei, China;Department of Computer Science, Navy University of Engineering, Wuhan, Hubei, China;Department of Computer Science, Navy University of Engineering, Wuhan, Hubei, China;Department of Computer Science, Navy University of Engineering, Wuhan, Hubei, China;Institute of Noise and Vibration, Navy University of Engineering, Wuhan, Hubei, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part III
Year:
2005

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Abstract

In this paper, neural network approaches are for the first time employed to identify acoustic fault sources of underwater vehicles. According to the characteristics of acoustic fault sources, a novel sources identification model based on modular structure variable radial basis function (SVRBF) network is proposed. Unsupervised algorithms for clustering and supervised learning are combined to train the network, especially, the number of hidden and output layer neurons can be modified on-line so that the network has the capability of incremental learning. The results of experiment show that the proposed network has better generalization performance than traditional BP network and RBF network, and is effective in learning new fault patterns.