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

[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).