Frontiers of Information Technology & Electronic Engineering
>> 2017,
Volume 18,
Issue 12
doi:
10.1631/FITEE.1601395
Article
Battle damage assessment based on an improved Kullback-Leibler divergence sparse autoencoder
. 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.
Available online: 2018-03-08
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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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