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Journal of System Simulation

Abstract

Abstract: There are lots of uncertain elements in battlefields situation assessment and the uncertainty simulation would enhance the ability of situation assessment. A deep variational autoencoder bayesian networks (BN) model with memory module is proposed aiming at the problem of being unable to represent the uncertainties exactly caused by the various combat objects and more uncertain elements. Based on the deep BN learning, the situation assessment model is designed from the deep generative model. The principle of deep generative model mixing with the memory module is discussed and the leaning and reasoning process of the model is explained. The proposed model is verified by an air strike BN construction example. The results show that the deep neural networks can approximate to the nonlinear transform of the latent variables and the designed outside memory module can store lots of local features extracted by the neural networks. The BN conditional probabilities are attained by the automatic learning and enhance the uncertainty simulation ability of BN.

First Page

79

Revised Date

2020-10-19

Last Page

85

CLC

TJ761.1;TP391

Recommended Citation

Nie Kai, Zeng Kejun, Meng Qinghai. Uncertainty Simulation Method Based on Deep Bayesian Networks Learning[J]. Journal of System Simulation, 2022, 34(1): 79-85.

DOI

10.16182/j.issn1004731x.joss.20-0629

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