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- W1497674509 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." @default.
- W1497674509 created "2016-06-24" @default.
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- W1497674509 date "2005-01-01" @default.
- W1497674509 modified "2023-10-16" @default.
- W1497674509 title "Identification of the Acoustic Fault Sources of Underwater Vehicles Based on Modular Structure Variable RBF Network" @default.
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- W1497674509 doi "https://doi.org/10.1007/11427469_91" @default.
- W1497674509 hasPublicationYear "2005" @default.
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