A stack sparse denoising autoencoder-based neural network approach for ship radiated noise target recognition

被引：0
|
作者：
Ju D. [1 ,2 ,3 ]
Li Y. [1 ,3 ]
Wang Y. [1 ,2 ,3 ]
Zhang C. [1 ,3 ]
机构：
[1] Institute of Acoustics, Chinses Academy of Sciences, Beijing
[2] University of Chinese Academy of Sciences, Beijing
[3] Key Laboratory of Science and Technology on Advanced Underwater Acoustic Signal Processing, Chinese Academy of Science, Beijing
来源：
Zhendong yu Chongji/Journal of Vibration and Shock | 2021年 / 40卷 / 24期
关键词：
Autoencoder; Feature extraction; Ship radiated noise; Target recognition;
D O I：
10.13465/j.cnki.jvs.2021.24.007
中图分类号：
学科分类号：
摘要：
The traditional feature extraction algorithm relies on the prior knowledge. Because it does not have the advantage of highlighting big data, the classification accuracy in practical application is poor and the generalization ability for different application scenarios is also obviously insufficient. In this paper, a deep learning algorithm was used for feature extraction of ship radiated noise, and a large number of classless data was fully utilized. The stack sparse self-encoder algorithm was to train the feature extraction neural network, and the Softmax classifier algorithm was used to fine-tune the parameters of the neural network by using class-based data. By comparing with the principal component analysis algorithm, the linear discriminant analysis algorithm, and the local linear embedding algorithm, it can be seen that the SSDAE-Softmax algorithm proposed in this paper can extract more discriminative features from ship radiated noise and improve the classification and recognition accuracy to some extent. © 2021, Editorial Office of Journal of Vibration and Shock. All right reserved.
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页码：50 / 56and74
页数：5624
相关论文
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