Study on Bayesian network structure learning algorithm based on ant colony node order optimization

被引：0

作者：

Liu, Haoran ^{[1
]}

Sun, Meiting ^{[1
]}

Li, Lei ^{[2
]}

Liu, Yongji ^{[1
]}

Liu, Bin ^{[1
]}

机构：

[1] School of Information Science and Engineering, Yanshan University, Qinhuangdao,066004, China

[2] School of Electrical Engineering, Yanshan University, Qinhuangdao,066004, China

来源：

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument | 2017年 / 38卷 / 01期

关键词：

Ant colony optimization - Fault detection - Cements - Forestry - Inference engines - Structural optimization - Trees (mathematics) - Bayesian networks;

D O I：

暂无

中图分类号：

学科分类号：

摘要：

K2 algorithm is the classical learning algorithm of Bayesian network structure. Aiming at the problems that K2 algorithm depends on the maximum number of parent nodes & node order and ant colony optimization algorithm has large search space, this paper proposes a new Bayesian structure learning algorithm - MWST-ACO-K2 algorithm. Firstly, through calculating the mutual information, the algorithm establishes the Most Weight Supported Tree (MWST) and obtain the maximum number of parent nodes. Secondly, ant colony optimization algorithm is adopted to search the Most Weight Supported Tree and obtain the node order. Finally, combining with K2 algorithm, the proposed algorithm can obtain the optimal Bayesian network structure. The simulation experiment results show that the proposed algorithm not only solves the problem that K2 algorithm relies on prior knowledge, but also reduces the search space of ant colony algorithm, simplifies the search mechanism and obtains good Bayesian structure. The proposed algorithm was applied to the operation data of the cement rotary kiln in Jidong Cement Company, established the Bayesian network structure model of the cement rotary kiln and achieved precise and rapid fault diagnosis. © 2017, Science Press. All right reserved.

引用

页码：143 / 150