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基于神经网络集成的声学故障识别 被引量：1

Acoustic Fault Identification Based on Neural Network Ensemble
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摘要 提出一种基于神经网络集成的水下航行器声学故障识别方法。该方法把经单独训练的具有一定差异度的单个RBF神经网络加以集成,可提高分类器的泛化性能。实验证明该模型的有效性。 A method for acoustic fault identification was presented based on neural network ensemble. In the method ; those single RBF neural networks which had been trained solely were integrated. It improved generalization performance of classifier. The experiment results show the model is effective.
作者 高志华 贲可荣 田立业 崔立林
机构地区 海军工程大学计算机工程系 海军工程大学振动与噪声研究所
出处 《船海工程》 2009年第2期86-89,共4页 Ship & Ocean Engineering
基金 国家自然科学基金(50775218)
关键词 神经网络集成 小样本 泛化性 差异性 neural network ensemble small data set generalization diversity
分类号 U674.7 [交通运输工程—船舶及航道工程]
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参考文献7

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共引文献8

同被引文献7

  • 1章林柯,何琳,朱石坚.潜艇主要噪声源识别方法研究[J].噪声与振动控制,2006,26(4):7-10. 被引量：27
  • 2田立业.基于增量型神经网络的噪声源识别研究:[硕士学位论文] [D].武汉:海军工程大学,2008.
  • 3Gao Zhihua,Ben Kerong,Cui Lilin.Underwater Vehicle Noise Source Recognition Using Structure Dynamic Adjustable SVM[C] ∥Proc of the 2009 Int'l Conf on Autonomic and Trusted Computing,2009:423-427.
  • 4扬小兵.聚类分析中若干关键技术的研究:[博士学位论文] [D].杭州:浙江大学,2005.
  • 5Sambasivam S,Theodosopoulos N.Advanced Data Clustering Methods of Mining Web Documents[J].Informing Science and Information Technology,2006,3:563-579.
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  • 7孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008(1):48-61. 被引量：1063

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船海工程
2009年 第2期

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