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

QI Zong-feng; LIU Qiao-qiao; WANG Jun; LI Jian-xun

doi:10.1631/FITEE.1601395

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

Regular Papers | Updated：2022-06-06

- Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder
- 基于改进Kullback-Leibler散度稀疏自动编码机的战损评估
- Frontiers of Information Technology & Electronic Engineering Vol. 18, Issue 12, Pages: 1991-2000(2017)
- Affiliations：
  
  State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, Luoyang 471003, China
  MOE Key Laboratory of System Control and Information Processing, Shanghai Jiao Tong University, Shanghai 200240, China
  Luoyang Electronic Equipment Test Center of China, Luoyang 471000, China
- Author bio：
  
  Jian-xun LI, E-mail: lijx@sjtu.edu.cn
- Funds：
- DOI：10.1631/FITEE.1601395
  CLC： TP391.4;E917
- Received：04 July 2016，
  
  Revised：2017-12-20，
  
  Published：2017-12
- Accepted：
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QI Zong-feng, LIU Qiao-qiao, WANG Jun, et al. Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 1991-2000.
DOI：

QI Zong-feng, LIU Qiao-qiao, WANG Jun, et al. Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(12): 1991-2000. DOI： 10.1631/FITEE.1601395.

摘要

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。

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.

关键词

Keywords

references

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