Advertisement

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

  • Zong-feng Qi
  • Qiao-qiao Liu
  • Jun Wang
  • Jian-xun Li
Article
  • 40 Downloads

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.

Key words

Battle damage assessment Improved Kullback-Leibler divergence sparse autoencoder Structural optimization Feature selection 

CLC number

TP391.4 E917 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Cao, S.C., Zhang, F., 2014. Review of battle damage assessment. Mil. Econ. Res., (8):53–56 (in Chinese).Google Scholar
  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. https://doi.org/10.1109/RAMECH.2011.6070499Google Scholar
  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. https://doi.org/10.1631/FITEE.1500439CrossRefGoogle Scholar
  4. Hastie, T., Tibshirani, R., Friedman, J., 2009. The Elements of Statistical Learning (2nd Ed.). Springer, New York, USA. https://doi.org/10.1007/978-0-387-84858-7CrossRefGoogle Scholar
  5. Hosmer, D.W., Lemeshow, S., 2005. Applied Logistic Regression (2nd Ed.). John Wiley & Sons, New York, USA. https://doi.org/10.1002/0471722146MATHGoogle Scholar
  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.CrossRefGoogle Scholar
  7. Jensen, F.V., Nielsen, T.D., 2007. Bayesian Networks and Decision Graphs. Springer, New York, USA. https://doi.org/10.1007/978-0-387-68282-2CrossRefGoogle Scholar
  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. https://doi.org/10.1109/IJCNN.2015.7280568Google Scholar
  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. https://doi.org/10.1109/ICSESS.2014.6933622Google Scholar
  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). https://doi.org/10.7690/bgzdh.2016.06.017Google Scholar
  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. https://doi.org/10.1109/SNPD.2007.421Google Scholar
  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. https://doi.org/10.1631/jzus.C1300185CrossRefGoogle Scholar
  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.Google Scholar
  14. Seber, G.A.F., Lee, A.J., 2012. Linear Regression Analysis (2nd Ed.). John Wiley & Sons, New York, USA. https://doi.org/10.1002/9780471722199MATHGoogle Scholar
  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. https://doi.org/10.1631/FITEE.1500346CrossRefGoogle Scholar
  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). https://doi.org/10.3969/j.issn.1672-9730.2016.01.009MathSciNetGoogle Scholar
  17. Vens, C., Struyf, J., Schietgat, L., et al., 2008. Decision trees for hierarchical multi-label classification. Mach. Learn., 73:185–214. https://doi.org/10.1007/s10994-008-5077-3CrossRefGoogle Scholar
  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.MathSciNetMATHGoogle Scholar
  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). https://doi.org/10.11817/j.issn.1672-7207.2016.05.018Google Scholar
  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).Google Scholar
  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. https://doi.org/10.1109/ICCIS.2012.298Google Scholar
  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). https://doi.org/10.16208/j.issn1000-7024.2015.04.036Google Scholar

Copyright information

© Zhejiang University and Springer-Verlag GmbH Germany, part of Springer Nature 2017

Authors and Affiliations

  1. 1.State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information SystemLuoyangChina
  2. 2.MOE Key Laboratory of System Control and Information ProcessingShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Luoyang Electronic Equipment Test Center of ChinaLuoyangChina

Personalised recommendations