TY - GEN
T1 - Method of Learning Dynamic Bayesian Network Parameter Based on DEQPK Algorithm
AU - Li, Weinan
AU - Shi, Jingping
AU - Zhang, Weiguo
AU - Wu, Yunyan
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Differential evolution
KW - Dynamic bayesian network
KW - Parameter learning
KW - Qualitative prior knowledge
KW - Situation assessment
UR - http://www.scopus.com/inward/record.url?scp=85151152840&partnerID=8YFLogxK
U2 - 10.1007/978-981-19-6613-2_138
DO - 10.1007/978-981-19-6613-2_138
M3 - 会议稿件
AN - SCOPUS:85151152840
SN - 9789811966125
T3 - Lecture Notes in Electrical Engineering
SP - 1402
EP - 1412
BT - Advances in Guidance, Navigation and Control - Proceedings of 2022 International Conference on Guidance, Navigation and Control
A2 - Yan, Liang
A2 - Duan, Haibin
A2 - Deng, Yimin
A2 - Yan, Liang
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Guidance, Navigation and Control, ICGNC 2022
Y2 - 5 August 2022 through 7 August 2022
ER -