CLC number: TP391.4; E917
On-line Access: 2024-08-27
Received: 2023-10-17
Revision Accepted: 2024-05-08
Crosschecked: 2017-12-20
Cited: 0
Clicked: 6407
Zong-feng Qi, Qiao-qiao Liu, Jun Wang, Jian-xun Li. Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 1991-2000.
@article{title="Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder",
author="Zong-feng Qi, Qiao-qiao Liu, Jun Wang, Jian-xun Li",
journal="Frontiers of Information Technology & Electronic Engineering",
volume="18",
number="12",
pages="1991-2000",
year="2017",
publisher="Zhejiang University Press & Springer",
doi="10.1631/FITEE.1601395"
}
%0 Journal Article
%T Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder
%A Zong-feng Qi
%A Qiao-qiao Liu
%A Jun Wang
%A Jian-xun Li
%J Frontiers of Information Technology & Electronic Engineering
%V 18
%N 12
%P 1991-2000
%@ 2095-9184
%D 2017
%I Zhejiang University Press & Springer
%DOI 10.1631/FITEE.1601395
TY - JOUR
T1 - Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder
A1 - Zong-feng Qi
A1 - Qiao-qiao Liu
A1 - Jun Wang
A1 - Jian-xun Li
J0 - Frontiers of Information Technology & Electronic Engineering
VL - 18
IS - 12
SP - 1991
EP - 2000
%@ 2095-9184
Y1 - 2017
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/FITEE.1601395
Abstract: The nodes number of the hidden layer in a deep learning network is quite difficult to determine with traditional methods. To solve this problem, an improved Kullback-Leibler divergence sparse autoencoder (KL-SAE) is proposed in this paper, which can be applied to battle damage assessment (BDA). This method can select automatically the hidden layer feature which contributes most to data reconstruction, and abandon the hidden layer feature which contributes least. Therefore, the structure of the network can be modified. In addition, the method can select automatically hidden layer feature without loss of the network prediction accuracy and increase the computation speed. Experiments on University of California-Irvine (UCI) data sets and BDA for battle damage data demonstrate that the method outperforms other reference data-driven methods. The following results can be found from this paper. First, the improved KL-SAE regression network can guarantee the prediction accuracy and increase the speed of training networks and prediction. Second, the proposed network can select automatically hidden layer effective feature and modify the structure of the network by optimizing the nodes number of the hidden layer.
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