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.
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References
Yang, D.: Noise Sources Identification and Analysis for Underwater Vehicles. PhD thesis, Harbin university of engineering, Harbin (1998)
Lampariello, F., Sciandrone, M.: Efficient Training of RBF Neural Networks for Pattern Recognition. IEEE Transactions on Neural Networks 12, 1235–1242 (2000)
Mak, M.W., Kung, S.Y.: Estimation of Elliptical Basis Function Parameters by the EM Algorithm with Application to Speaker Verification. IEEE Transactions on Neural Networks 11, 961–969 (2000)
Bishop, C.M.: Neural Networks for Pattern Recognition. Clarendon Press, Oxford (1995)
Gao, X., Lu, J.: Target Recognition Algorithm for Passive Sonar System with High Generalization Ability. ACTA ACUSTICA 23, 213–220 (1998)
Schwenker, F.: Radial-Basis-Function Networks: Learning and Applications. In: Fourth International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies, Brighton, pp. 33–43 (2000)
Fang, S.: A Hidden Layer-structure-adaptive Neural Network Used in Underwater Acoustic Target Recognition. Journal of Southeast University 29, 89–94 (1999)
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© 2005 Springer-Verlag Berlin Heidelberg
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Zhang, L., He, L., Ben, K., Wei, N., Pang, Y., Zhu, S. (2005). Identification of the Acoustic Fault Sources of Underwater Vehicles Based on Modular Structure Variable RBF Network. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3498. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427469_91
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DOI: https://doi.org/10.1007/11427469_91
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25914-5
Online ISBN: 978-3-540-32069-2
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