[1]高晓利[],李捷[][]. 基于模糊变结构动态贝叶斯网的目标识别方法[J].计算机技术与发展,2017,27(09):17-21.
　GAO Xiao-li[],LI Jie[][]. A Target Identification Method of Dynamic Bayesian Network with Fuzzy Variable Structure[J].,2017,27(09):17-21.
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 基于模糊变结构动态贝叶斯网的目标识别方法()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
27
期数:
2017年09期
页码:
17-21
栏目:
智能、算法、系统工程
出版日期:
2017-09-10

文章信息/Info

Title:
 A Target Identification Method of Dynamic Bayesian Network with Fuzzy Variable Structure
文章编号:
1673-629X（2017)09-0017-05
作者:
 高晓利[1] ; 李捷[1][2]
 1.四川九洲电器集团有限责任公司；电子科技大学 通信抗干扰国家重点实验室
Author(s):
 GAO Xiao-li[1] ; LI Jie[1][2]
关键词:
 ; 数据融合; 目标识别; 贝叶斯网络; 结构学习; 参数学习
Keywords:
 ; data fusion; target identification; Bayesian network; structure learning; parameter learning
分类号:
TP274
文献标志码:
A
摘要:
 在研究分析各信源信息的特征和目标识别的基本流程的基础上,基于传统静态贝叶斯网络,提出了一种基于模糊变结构动态贝叶斯网络的目标识别方法.该方法构建了模糊变结构动态贝叶斯网络,并提出了基于样本信息的统计方法和无样本的贝叶斯网络参数学习方法,以期网络推理实现目标属性识别,并在传统硬判决的基础上,实现了基于软判决准则的动态判决和基于线性加权思想的网络参数在线更新.数值实验表明,相对于传统静态贝叶斯网络目标识别方法,所提出的方法能够解决不同时刻证据的时序关系问题以及不能处理连续随机变量推理的问题,提高了目标识别置信度,缩短了识别收敛周期,能够有效纠正关联错误和关联多义性所造成的错误识别问题,解决了网络参数一成不变的问题,较好地实现了网络参数的在线更新.
Abstract:
 y researching and analyzing the characteristics of the source information and the basic process of target identification,on the basis of traditional static Bayesian network,a method of target identification based on fuzzy variable structure dynamic Bayesian network is proposed. It constructs the fuzzy variable structure dynamic Bayesian network and proposes a statistical method based on sample infor-mation and a learning method of sample-free Bayesian network parameters for implementation of target identification according to net-work inference and application of traditional hard decision. The dynamic decision has been performed based on the soft decision principles and the network parameters’ update online is finished based on liner weighting theory. Compared with traditional static Bayesian network for target identification, it has solved the issues such as the sequential relationship of evidence at different time and the networks inference of constant random variables. Meanwhile it has not only improved the confidence coefficient of target identification but also shortened the identification convergence period and effectively resolved error identification problem caused by error or ambiguity association. In addi-tion,the problem of network parameters unchanged has been solved and the network parameters’ update online has also been completed.

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更新日期/Last Update: 2017-10-19