[1]方世良.一种应用于水声目标识别的隐层结构自适应网络[J].东南大学学报(自然科学版),1999,29(3):89-94.[doi:10.3969/j.issn.1001-0505.1999.03.017]
 Fang Shiliang.A Hidden-layer-structure-adaptive Neural Network Used in Underwater Acoustic Target Recognition[J].Journal of Southeast University (Natural Science Edition),1999,29(3):89-94.[doi:10.3969/j.issn.1001-0505.1999.03.017]
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一种应用于水声目标识别的隐层结构自适应网络()
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《东南大学学报(自然科学版)》[ISSN:1001-0505/CN:32-1178/N]

卷:
29
期数:
1999年第3期
页码:
89-94
栏目:
仪器科学与技术
出版日期:
1999-05-20

文章信息/Info

Title:
A Hidden-layer-structure-adaptive Neural Network Used in Underwater Acoustic Target Recognition
作者:
方世良
东南大学无线电工程系,南京 210096
Author(s):
Fang Shiliang
Department of Radio Engeneering,Southeast University, Nanjing 210096
关键词:
目标分类 神经网络 径向基 水声
Keywords:
target classification neural network radial basis underwater acoustics
分类号:
U666.7
DOI:
10.3969/j.issn.1001-0505.1999.03.017
摘要:
提出了一种隐层结构自适应学习的径向基函数网络(HSARBF)水声目标分类器. 该网络可在训练中自适应调整隐层节点数和设置新增隐节点的初始权值,从而使网络输入样本的分类特征能有效地映射到隐节点输出,克服了一般RBF网隐层初始权值及隐节点数难以确定的缺陷. 经对实测水声信号的识别试验表明,该网络隐层有较强的分类特征划分能力,识别率高于一般RBF网或BP网分类器.
Abstract:
This paper proposes a hidden-layer-structure-adaptive radial basis function (HSARBF) classifier. The number of hidden nodes can be adaptively adjusted and the initial weights jointed to additional nodes can be automatically assigned in the new method. Therefore, the class features of input samples can be effectively mapped to hidden layer. A few difficulties, as assigning initial weights and determing the number of hidden nodes in a traditional RBF classifier, can be well overcome. The effective division in feature space and high recognition rate of the classifier are demonstrated with experiments for real underwater acoustic signals.

参考文献/References:

[1] Urick R J.水声原理.洪申译.哈尔滨:哈尔滨船舶工程学院出版社,1990.260~276
[2] Lippmann R P.Pattern classification using neural networks.IEEE Communications Magazine,1989(11):47~64
[3] Musavi M T.On the training of radial basis function classifiers.Neural Networks,1992(5):595~603
[4] Chinrungrueng C,Sequin C H.Optimal adaptive k-means algorithm with dynamic adjustment of learning rate.IEEE Transactions on Neural Networks,1995(6):157~168
[5] 方世良.一个聚类数动态可调的水声信号聚类算法.声学学报,1996,21(4):525~530

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备注/Memo

备注/Memo:
第一作者:男,1960年生,硕士,副教授.
更新日期/Last Update: 1999-05-20