Bayesian Network Reconstruction and Iterative Divergence Problem Solving Method Based on Norm Minimization  

作　　者：Kuo Li Aimin Wang Limin Wang Yuetan Zhao Xinyu Zhu 

机构地区：[1]Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education,Jilin University,Changchun,130012,China [2]College of Software,Jilin University,Changchun,130012,China

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期617-637,共21页工程与科学中的计算机建模(英文)

基　　金：supported by the Scientific and Technological Developing Scheme of Jilin Province,China(No.20240101371JC);the National Natural Science Foundation of China(No.62107008).

摘　　要：A Bayesian network reconstruction method based on norm minimization is proposed to address the sparsity and iterative divergence issues in network reconstruction caused by noise and missing values.This method achieves precise adjustment of the network structure by constructing a preliminary random network model and introducing small-world network characteristics and combines L1 norm minimization regularization techniques to control model complexity and optimize the inference process of variable dependencies.In the experiment of game network reconstruction,when the success rate of the L1 norm minimization model’s existence connection reconstruction reaches 100%,the minimum data required is about 40%,while the minimum data required for a sparse Bayesian learning network is about 45%.In terms of operational efficiency,the running time for minimizing the L1 normis basically maintained at 1.0 s,while the success rate of connection reconstruction increases significantly with an increase in data volume,reaching a maximum of 13.2 s.Meanwhile,in the case of a signal-to-noise ratio of 10 dB,the L1 model achieves a 100% success rate in the reconstruction of existing connections,while the sparse Bayesian network had the highest success rate of 90% in the reconstruction of non-existent connections.In the analysis of actual cases,the maximum lift and drop track of the research method is 0.08 m.The mean square error is 5.74 cm^(2).The results indicate that this norm minimization-based method has good performance in data efficiency and model stability,effectively reducing the impact of outliers on the reconstruction results to more accurately reflect the actual situation.

关 键 词：Bayesian norm minimization network reconstruction iterative divergence SPARSITY 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]