•  
  •  
 

Journal of System Simulation

Abstract

Abstract: Aiming at the problem of low accuracy of BN parameter learning due to the uncertainty of a single expert prior knowledge under the condition of small sample data set, a BN parameter learning method based on AHP-DST fusion expert prior knowledge was designed. The synthetic prior knowledge of experts was calculated by using the thought of analytic hierarchy process combined with the rules of evidence theory synthesis. The expert comprehensive prior knowledge was added to the normal distribution and combined with the monotonicity constraint to obtain the virtual sample information. The virtual sample information was added to the Bayesian estimation to get the network parameter estimate. The simulation results show that the KL divergence of the proposed method is always better than that of the other four methods when the sample data is small, and the running time is slightly higher than that of the other two methods. In general, the comprehensive performance of the proposed algorithm outperforms the other four methods, and it is more suitable for the case of small sample data. The proposed method is applied to the identification of the attack intention of the air target on the surface ship, and the simulation results can reflect the actual situation well, which further verifies the effectiveness and feasibility of the method.

First Page

2778

Last Page

2792

CLC

TP181

Recommended Citation

Chen Haiyang, Lin Hongkai, Ren Zhifang, et al. Bayesian Network Parameter Learning Based on AHPDST Fusion of Expert Prior Knowledge[J]. Journal of System Simulation, 2025, 37(11): 2778-2792.

Corresponding Author

Lin Hongkai

DOI

10.16182/j.issn1004731x.joss.24-0658

Download

Share

COinS