一种基于ISODATA聚类和改进相似度的证据推理方法

李新德; 王丰羽

doi:10.16383/j.aas.2015.c140543

一种基于ISODATA聚类和改进相似度的证据推理方法

doi: 10.16383/j.aas.2015.c140543

李新德^,,
王丰羽

1.
东南大学自动化学院复杂工程测量与控制教育部重点实验室南京 210096

基金项目:

国家自然科学基金 (60804063, 61175091), 江苏省 “青蓝工程” 资助计划, 航空基金 (20140169002), 江苏省 “六大高峰人才” 资助计划,江苏高校优势学科建设工程资助项目资助

详细信息

作者简介:
王丰羽东南大学自动化学院硕士研究生.主要研究方向为信息融合和不确定推理. E-mail: treexiaohei@163.com

通讯作者:
李新德东南大学自动化学院副教授.主要研究方向为智能机器人, 人机交互, 机器感知, 信息融合, 不确定推理和机器视觉.本文通信作者. E-mail: xindeli@seu.edu.cn

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出版历程
- 收稿日期: 2014-07-18
- 修回日期: 2014-09-27
- 刊出日期: 2015-03-20

A Method of Evidence Reasoning Based on ISODATA Clustering and Improved Similarity Measure

LI Xin-De^,,
WANG Feng-Yu

1.
Key Laboratory of Measurement and Control of Complex Systems of Engineering of Ministry of Education, School of Automation, Southeast University, Nanjing 210096

Funds:

Supported by National Natural Science Foundation of China (60804063, 61175091), Qing Lan Project of Jiangsu Province, Aeronautical Science Foundation of China (20140169002), Six Major Top-talent Plan of Jiangsu Province, and A Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

摘要

摘要: 针对智能信息处理中Dempster组合规则不能处理高度冲突的问题, 从内、外证据不确定性分析的角度深入揭示了证据冲突产生的原因, 即证据的冲突性不仅仅根源于证据间的矛盾, 也与证据自身的不确定性密切相关, 提出了一种同时考虑证据自冲突和外部冲突的相似性测度, 然后利用新测度计算证据的众信度, 对证据源进行修正;与此同时, 根据原始证据间的聚类特性, 利用迭代自组织数据分析技术(Iterative selforganizing data analysis techniques algorithm, ISODATA)聚类方法进行聚类, 然后利用Dempster组合规则合成每一聚类中所有证据为证据代表, 并综合众信度和证据在该聚类的频度计算可靠度, 最后, 利用统一组合规则合成证据代表.并通过大量的算例, 同其他方法和自身改进前后进行深入比较, 优势比较明显, 有效地解决了冲突证据合成出现的问题.
- 证据推理 /
- 冲突 /
- 聚类 /
- 相似测度 /
- 组合规则
Abstract: For the problem that the highly conflictive evidences can not be processed by Dempster rule in intelligent information processing, the reason of conflict occurrence is further revealed by analysis of uncertainty among evidences and evidences themselves. A similarity measure by considering self-conflict and external conflict of evidence together is proposed and used to compute the commonality of evidence. All initial evidences can be revised according to the commonality. At the same time, according to the clustering characteristic of initial evidences, they are clustered by applying ISODATA (Iterative selforganizing data analysis techniques algorithm), and then, all evidences in each cluster are combined as a representative of evidences by applying Dempster rule. The reliability of the representative of evidence is computed by considering the credibility and frequency of occurrence of the evidence in a cluster together. Finally, these representatives of evidences are combined by using the unified combination rule. Several numerical examples are given to deeply compare this method with others and itself before improving its similarity measure. The result shows that the new method has a distinct advantage over others and can effectively solve the problem of combination of conflictive evidences.
- Evidence reasoning /
- conflict /
- clustering /
- similarity measure /
- combination rule

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参考文献(37)

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一种基于ISODATA聚类和改进相似度的证据推理方法

doi: 10.16383/j.aas.2015.c140543

作者简介:
王丰羽东南大学自动化学院硕士研究生.主要研究方向为信息融合和不确定推理. E-mail: treexiaohei@163.com

通讯作者:
李新德东南大学自动化学院副教授.主要研究方向为智能机器人, 人机交互, 机器感知, 信息融合, 不确定推理和机器视觉.本文通信作者. E-mail: xindeli@seu.edu.cn

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A Method of Evidence Reasoning Based on ISODATA Clustering and Improved Similarity Measure

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留言板

一种基于ISODATA聚类和改进相似度的证据推理方法

doi: 10.16383/j.aas.2015.c140543

作者简介: 王丰羽 东南大学自动化学院硕士研究生.主要研究方向为信息融合和不确定推理. E-mail: treexiaohei@163.com

通讯作者: 李新德 东南大学自动化学院副教授.主要研究方向为智能机器人, 人机交互, 机器感知, 信息融合, 不确定推理和机器视觉.本文通信作者. E-mail: xindeli@seu.edu.cn

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出版历程

A Method of Evidence Reasoning Based on ISODATA Clustering and Improved Similarity Measure

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出版历程

目录

作者简介:
王丰羽东南大学自动化学院硕士研究生.主要研究方向为信息融合和不确定推理. E-mail: treexiaohei@163.com

通讯作者:
李新德东南大学自动化学院副教授.主要研究方向为智能机器人, 人机交互, 机器感知, 信息融合, 不确定推理和机器视觉.本文通信作者. E-mail: xindeli@seu.edu.cn