[1]么洪飞,王宏健,王莹,等.基于遗传算法DDBN参数学习的UUV威胁评估[J].哈尔滨工程大学学报,2018,39(12):1972-1978.[doi:10.11990/jheu.201711072]
 YAO Hongfei,WANG Hongjian,WANG Ying,et al.Threat assessment of UUV based on genetic algorithm DDBN parameter learning[J].hebgcdxxb,2018,39(12):1972-1978.[doi:10.11990/jheu.201711072]
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基于遗传算法DDBN参数学习的UUV威胁评估(/HTML)
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《哈尔滨工程大学学报》[ISSN:1006-6977/CN:61-1281/TN]

卷:
39
期数:
2018年12期
页码:
1972-1978
栏目:
出版日期:
2018-12-05

文章信息/Info

Title:
Threat assessment of UUV based on genetic algorithm DDBN parameter learning
作者:
么洪飞12 王宏健1 王莹13 李庆1
1. 哈尔滨工程大学 自动化学院, 黑龙江 哈尔滨 150001;
2. 齐齐哈尔大学 机电工程学院, 黑龙江 齐齐哈尔 161006;
3. 国网黑龙江省电力有限公司 经济技术研究院, 黑龙江 哈尔滨 150036
Author(s):
YAO Hongfei12 WANG Hongjian1 WANG Ying13 LI Qing1
1. College of Automation, Harbin Engineering University, Harbin 150001;
2. College of Mechanical Engineering, Qiqihar University, Qiqihar 161006, China;
3. Economic Research Institute, State Grid Heilongjiang Electric Power Company Limited, Harbin 150036, China
关键词:
参数学习威胁评估离散动态贝叶斯网络遗传算法无人水下航行器决策
分类号:
U666
DOI:
10.11990/jheu.201711072
文献标志码:
A
摘要:
针对复杂海洋环境下存在不确定事件对无人水下航行器自主作业和安全性所带来的威胁。本文设计了基于动态贝叶斯网络的威胁评估模型和决策推理模型,采用遗传算法实现了离散动态贝叶斯网络参数学习,最终得到最优的模型参数,进而增强了推理模型对海洋环境的快速反应能力。仿真实验结果证明:提出的算法可以得到真实的离散动态贝叶斯网络参数,能够有效地解决复杂海洋环境下UUV威胁评估问题,为UUV的自主任务决策提供有效的参数保障。

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备注/Memo

备注/Memo:
收稿日期:2017-11-21。
基金项目:国家自然科学基金重点项目(61633008);黑龙江省自然科学基金面上项目(F2015035);哈尔滨市科技创新人才(优秀学科带头人)研究专项基金项目(2012RFXXG083).
作者简介:么洪飞(1980-),男,讲师,博士研究生;王宏健(1971-),女,教授,博士生导师.
通讯作者:王宏健,E-mail:cctime99@163.com.
更新日期/Last Update: 2018-12-01