Full Text:   <908>

Summary:  <718>

CLC number: TP391.4; E917

On-line Access: 2018-02-06

Received: 2016-07-04

Revision Accepted: 2017-01-23

Crosschecked: 2017-12-20

Cited: 0

Clicked: 2870

Citations:  Bibtex RefMan EndNote GB/T7714

 ORCID:

Zong-feng Qi

http://orcid.org/0000-0001-7031-8477

-   Go to

Article info.
Open peer comments

Frontiers of Information Technology & Electronic Engineering  2017 Vol.18 No.12 P.1991-2000

http://doi.org/10.1631/FITEE.1601395


Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder


Author(s):  Zong-feng Qi, Qiao-qiao Liu, Jun Wang, Jian-xun Li

Affiliation(s):  State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China; more

Corresponding email(s):   lijx@sjtu.edu.cn

Key Words:  Battle damage assessment, Improved Kullback-Leibler divergence sparse autoencoder, Structural optimization, Feature selection


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.

基于改进Kullback-Leibler散度稀疏自动编码机的战损评估

概要:为解决深度学习网络中隐藏层节点数难以确定的问题,文中提出一种改进的KL(Kullback-Leibler)散度稀疏自动编码机,并将该方法应用到战斗损伤评估中。该方法能够自动筛选出对数据重建贡献大的隐层特征,舍弃贡献小的隐层特征,从而优化网络结构。在网络预测精度不受影响的前提下,该方法自动筛选隐层特征,提升了计算速度。基于UCI(University of California, Irvine)数据集和BDA(battle damage assessment)战争破坏数据的实验表明,该方法优于其他数据驱动的方法。改进的KL稀疏自动编码机回归网络在保证预测精度的前提下,能提升网络的训练和预测速度,并自动筛选隐层有效特征,优化隐层节点数,优化网络结构。

关键词:战场损伤评估;改进的KL散度稀疏自动编码机;结构优化;特征选择

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article

Reference

[1]Cao, S.C., Zhang, F., 2014. Review of battle damage assessment. Mil. Econ. Res., (8):53-56 (in Chinese).

[2]Chen, X., Li, L., Liu, D., 2011. Battle damage level prediction on fuzzy theory and Bayesian method. IEEE Conf. on Robotics, Automation and Mechatronics, p.295-298.

[3]Ding, Y., Li, N., Zhao, Y., et al., 2016. Image quality assessment method based on nonlinear feature extraction in kernel space. Front. Inform. Technol. Electron. Eng., 17(10):1008-1017.

[4]Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning. Springer, New York, USA.

[5]Hosmer, D.W., Lemeshow, S., 2005. Applied Logistic Regression. John Wiley & Sons, New York, USA.

[6]Hubel, D.H., Wiesel, T.N., 1962. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. J. Physiol., 160(1):106-154.

[7]Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. Springer, New York, USA.

[8]Jiang, N., Rong, W.G., Peng, B.L., et al., 2015. An empirical analysis of different sparse penalties for autoencoder in unsupervised feature learning. Int. Joint Conf. on Neural Networks, p.1-8.

[9]Li, C.H., Huang, J., 2014. The application of Bayesian network in battle damage assessment. IEEE Int. Conf. on Software Engineering and Service Science, p.529-532.

[10]Ma, X.M., Ding, P., Yan, W.D., 2016. Warship-damage assessment based on Bayesian networks. Ordnance Ind. Autom., 35(6):72-75 (in Chinese).

[11]Ma, Z.J., Shi, Q., Li, B., 2007. Battle damage assessment based on Bayesian network. 8th ACIS Int. Conf. on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, p.388-391.

[12]Qin, F.W., Li, L.Y., Gao, S.M., et al., 2014. A deep learning approach to the classification of 3D CAD models. J. Zhejiang Univ.-Sci. C (Comput. & Electron.), 15(2):91-106.

[13]Rifai, S., Vincent, P., Muller, X., et al., 2011. Contractive auto-encoders: explicit invariance during feature extraction. 28th Int. Conf. on Machine Learning, p.833-840.

[14]Seber, G.A.F., Lee, A.J., 2012. Linear Regression Analysis. John Wiley & Sons, New York, USA.

[15]Song, G.H., Jin, X.G., Chen, G.L., et al., 2016. Two-level hierarchical feature learning for image classification. Front. Inform. Technol. Electron. Eng., 17(9):897-906.

[16]Sun, G.L., Li, J., 2016. Battle damage assessment based on attribute weighted Bayesian classification. Ship Electron. Eng., 36(1):29-32 (in Chinese).

[17]Vens, C., Struyf, J., Schietgat, L., et al., 2008. Decision trees for hierarchical multi-label classification. Mach. Learn., 73:185-214.

[18]Vincent, P., Larochelle, H., Lajoie, I., et al., 2010. Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion. J. Mach. Learn. Res., 11(12):3371-3408.

[19]Wen, M.F., Hu, C., Liu, W.R., 2016. Heterogeneous multimodal object recognition method based on deep learning. J. Cent. South Univ. (Sci. Technol.), 47(5):1580-1586 (in Chinese).

[20]Yong, L.Y., 2004. Modeling in Battle Damage Based on Multi-agent. MS Thesis, Harbin University of Science and Technology, Harbin, China (in Chinese).

[21]Zhang, C., Shi, Q., Liu, T.L., et al., 2012. Study on battle damage level prediction using hybrid-learning algorithm. 4th Int. Conf. on Computational and Information Sciences, p.65-68.

[22]Zhao, Z.Y., Li, Y.X., Yu, F., et al., 2015. Improved deep learning algorithm based on extreme learning machine. Comput. Eng. Des., 36(4):1022-1026 (in Chinese).

Open peer comments: Debate/Discuss/Question/Opinion

<1>

Please provide your name, email address and a comment





Journal of Zhejiang University-SCIENCE, 38 Zheda Road, Hangzhou 310027, China
Tel: +86-571-87952783; E-mail: cjzhang@zju.edu.cn
Copyright © 2000 - Journal of Zhejiang University-SCIENCE