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【答案】应助回帖
★ ★ ★ ★ ★ ★ ★ ★
感谢参与，应助指数 +1
hylz1008: 金币+8 2014-12-13 21:08:06
oven1986: LS-EPI+1, 感谢应助。 2014-12-14 22:50:35
Accession number:
20134116842725
Title: Situation assessment using variable structure interval probability dynamic Bayesian network
Authors: Hu, Yun-An1 Email author hya507@sina.com; Liu, Zhen1, 2 Email author hylz1008@126.com; Shi, Jian-Guo3 Email author cherryapple@126.com
Author affiliation: 1 Department of Control Engineering, Naval Aeronautical and Astronautical University, Yantai 264001, China
2 Training Brigade of Equipment Acceptance and Modification, Naval Aeronautical and Astronautical University, Yantai 264001, China
3 Department of Ordnance Science and Technology, Naval Aeronautical and Astronautical University, Yantai 264001, China
Corresponding author: Hu, Y.-A. (hya507@sina.com)
Source title: Xi Tong Gong Cheng Yu Dian Zi Ji Shu/Systems Engineering and Electronics
Abbreviated source title: Xi Tong Cheng Yu Dian Zi Ji Shu/Syst Eng Electron
Volume: 35
Issue: 9
Issue date: September 2013
Publication year: 2013
Pages: 1891-1897
Language: Chinese
ISSN: 1001506X
CODEN: XGYDEM
Document type: Journal article (JA)
Publisher: Chinese Institute of Electronics, P.O. Box 165, Beijing, 100036, China
Abstract: The structure and the parameters of Bayesian network that is used for situation assessment are usually invariable in the past. In order to enhance the veracity of combat situation, a variable structure interval probability dynamic Bayesian network (VSIP-DBN) is proposed. The definition and the inference algorithm of the VSIP-DBN are given, the structure of VSIP-DBN can be varied according to the situation, and the rule of the network structure change is proposed. The parameters of the network are within the interval domain and the parameter learning method is also given. The air combat situation is assessed using VSIP-DBN. In the condition of interval probability parameter, even with incidental observation error, the simulation results show that the proposed model can accurately reflect the correct situation in the typical situations, so the proposed model enhance the flexibility of situation assessment.
Number of references: 15
Main heading: Bayesian networks
Controlled terms: Inference engines - Probability
Uncontrolled terms: Air combat situation - Dynamic Bayesian networks - Inference algorithm - Interval probability - Network structure change - Parameter learning - Situation assessment - Variable structures
Classification code: 723.4.1 Expert Systems - 921.4 Combinatorial Mathematics, Includes Graph Theory, Set Theory - 922.1 Probability Theory
DOI: 10.3969/j.issn.1001-506X.2013.09.15
Database: Compendex
Compilation and indexing terms, © 2014 Elsevier Inc. |
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