Method of Learning Dynamic Bayesian Network Parameter Based on DEQPK Algorithm

Weinan Li, Jingping Shi, Weiguo Zhang, Yunyan Wu

科研成果: 书/报告/会议事项章节会议稿件同行评审

摘要

For the problem of DBN parameter learning from small sample data, a differential evolution based on qualitative prior knowledge (DEQPK) is proposed. Firstly, the feasible region defined by the parameter qualitative constrains is sampled by the Monte Carlo to obtain the qualitative prior knowledge (QPK); secondly, the search space is reduced according to the QPK, and then the DE algorithm is used for parameter learning; finally, the QPK and the results of the DE algorithm are fused to acquire the real parameters. In the simulation experiment, three algorithms are used for parameter learning. The results show that the DEQPK is the most precise as well as the least time-consuming. At the same time, the parameters learned by the DEQPK algorithm is substituted into DBN, and the situations of battlefield targets are assessed.

源语言英语
主期刊名Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
编辑Liang Yan, Haibin Duan, Yimin Deng, Liang Yan
出版商Springer Science and Business Media Deutschland GmbH
1402-1412
页数11
ISBN(印刷版)9789811966125
DOI
出版状态已出版 - 2023
活动International Conference on Guidance, Navigation and Control, ICGNC 2022 - Harbin, 中国
期限: 5 8月 20227 8月 2022

出版系列

姓名Lecture Notes in Electrical Engineering
845 LNEE
ISSN(印刷版)1876-1100
ISSN(电子版)1876-1119

会议

会议International Conference on Guidance, Navigation and Control, ICGNC 2022
国家/地区中国
Harbin
时期5/08/227/08/22

指纹

探究 'Method of Learning Dynamic Bayesian Network Parameter Based on DEQPK Algorithm' 的科研主题。它们共同构成独一无二的指纹。

引用此