一种基于粗糙集证据理论深度融合的局部冲突快速合成方法

倪龙强;张丽华;姚新涛;胡高歌;刘鹏辉

兵工学报 ›› 2019, Vol. 40 ›› Issue (12) : 2560-2569.

兵工学报 ›› 2019, Vol. 40 ›› Issue (12) : 2560-2569. DOI: 10.3969/j.issn.1000-1093.2019.12.022
论文

一种基于粗糙集证据理论深度融合的局部冲突快速合成方法

  • 倪龙强1, 张丽华1, 姚新涛1, 胡高歌2, 刘鹏辉1
作者信息 +

A Deep Fusion Method Based on Rough Sets and Evidence Theory for Local Conflict Evidence Synthesis

  • NI Longqiang1, ZHANG Lihua1, YAO Xintao1, HU Gaoge2, LIU Penghui1
Author information +
文章历史 +

摘要

传统证据理论在进行多焦元属性融合时可能产生组合爆炸,当待合成证据体冲突时容易导致一票否决。针对以上两个问题,通过将待合成证据体转换为知识信息系统,应用粗糙集理论对多源信息进行规则约简,得到约简后的知识规则信息系统,计算出信息系统对每条证据体的支持度;在证据合成中,基于规则信息系统引入证据体支持度、焦元可信度、焦元一致性等因素来表示证据体之间的局部冲突,在此基础上给出冲突证据合成规则。通过测试算例对所提方法进行了测试验证,结果表明:该方法具有较快的收敛速度,计算时间消耗能够得到大幅度降低,在冲突证据合成中具有较强的鲁棒性。

Abstract

A local conflict allocation evidence combination rule based on rough sets theory is proposed to reduce the computational complexity and improve the efficiency in conflict evidence combination. In this proposed method, the information system which is consisted ofseveral evidences is reduced by rough sets, and the confidence of belief function is calculated based on the confidence of evidence and single focal elements individually. At last an example is given to prove the efficiency of this proposed method.Key

关键词

粗糙集 / 证据理论 / 数据融合 / 数据挖掘 / 冲突证据合成 / 信息系统

Key words

roughset / evidencetheory / datafusion / datamining / conflictevidencesynthesis / informationsystem

引用本文

导出引用
倪龙强, 张丽华, 姚新涛, 胡高歌, 刘鹏辉. 一种基于粗糙集证据理论深度融合的局部冲突快速合成方法. 兵工学报. 2019, 40(12): 2560-2569 https://doi.org/10.3969/j.issn.1000-1093.2019.12.022
NI Longqiang, ZHANG Lihua, YAO Xintao, HU Gaoge, LIU Penghui. A Deep Fusion Method Based on Rough Sets and Evidence Theory for Local Conflict Evidence Synthesis. Acta Armamentarii. 2019, 40(12): 2560-2569 https://doi.org/10.3969/j.issn.1000-1093.2019.12.022

基金

军委装备发展部装备预先研究基金项目(61403120205);中央高校基本科研业务费专项项目(3102018zy027)

参考文献



[1]KUSHWAHA, KUMAR S, MHEGDE R M. Multi-sensor data fusion methods for indoor activity recognition using temporal evidence theory[J]. Pervasive and Mobile Computing, 2015,21:19-29.
[2]ANDR C, HGARAT-MASCL S L, REYNAUD R. Evidential framework for data fusion in a multi-sensor surveillance system[J]. Engineering Applications of Artificial Intelligence,2015, 43(C):166-180.
[3]LU H, SHANGGUAN W B, YU D J. An imprecise probability approach for sequeal instability analysis based on evidence theory[J]. Journal of Sound and Vibration, 2017, 387: 96-113.
[4]JIANG W, ZHANG J. A modified combination rule in generalized evidence theory[J]. Applied Intelligence,2017, 46:630-640.
[5]DU W S, HU B Q. Attribute reduction in ordered decision tables via evidence theory[J]. Information Sciences,2016, 364: 91-110.
[6]郭晓陶,王星,周冬青.基于Dempster-Shafer证据理论的通信辐射源个体识别算法[J].兵工学报,2016,37(10): 1844-1851.
GUO X T,WANG X,ZHOU D Q.Individual communication transmitter identification based on dempster-shafer evidence theory[J].Acta Armamentarii,2016,37(10): 1844-1851. (in Chinese)
[7]苗成林,李彤,吕军,等.基于Dempster-Shafer证据理论与抗频谱感知数据篡改攻击的协作式频谱检测算法[J].兵工学报,2017,38(12): 2406-2413.
MIAO C L, LI T, L J,et al.Cooperative spectrum sensing algorithm against spectrum sensing data falsification based on dempster-shafer evidence theory[J].Acta Armamentarii,2017,38(12): 2406-2413. (in Chinese)
[8]ZADEH L A. A simple view of the Dempster-Shafer theory of evidence and its implication for the rule of combination[J]. AI Magazine, 1986, 7(2): 85-90.
[9]SMETS P. The combination of evidence in the transferable belief model[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1990, 12(5): 447-458.

[10]胡丽芳, 王晨熙, 朱靖, 等. 闭世界框架下灰色模糊多属性决策方法[J]. 控制与决策,2014,29(2): 246-250.
HU L F, WANG C X, ZHU J, et al. Approach for grey fuzzy MADA in closed world[J]. Control and Decision,2014,29(2): 246-250.(in Chinese)
[11]郭华伟, 施文康, 邓勇, 等. 证据冲突: 丢弃, 发现或化解?[J].系统工程与电子技术,2007,29(6): 890-897.
GUO H W, SHI W K, DENG Y, et al. Evidential conflict and its 3D strategy: discard, discover and disassemble?[J]. Systems Engineering and Electronics,2007,29(6): 890-897.(in Chinese)
[12]卢正才, 覃征. 证据合成的一般框架及高度冲突证据合成方法[J]. 清华大学学报(自然科学版),2011, 51(11): 1611-1615,1626.
LU Z C, QIN Z. General framework for evidence combination and its approach to highly conflicting evidence fusion[J]. Journal of Tsinghua University (Science and Technology),2011, 51(11):1611-1615,1626. (in Chinese)
[13]彭颖, 沈怀荣, 马永一. 一种新的冲突证据融合方法[J]. 兵工学报, 2011, 32(1): 78-84.
PENG Y, SHEN H R, MA Y Y. A new fusion method for conflicting evidence[J].Acta Armamentarii,2011, 32(1): 78-84.(in Chinese)
[14]DUY W, WANG Y M. Evidence combination rule with contrary support in the evidential reasoning approach[J]. Expert Systems with Applications,2017, 88(1): 193-204.
[15]MURPHYC K.Combining belief functions when evidence conflicts[J].Decision Support Systems,2000,29(1):1-9.
[16]徐凌宇,尹国成,宫义山,等.基于不同置信度的证据组合规则及应用[J].东北大学学报,2002, 23(2):123-125.
XU L Y,YIN G C,GONG Y S,et al.Combination rules of various credibility evidences and application [J].Journal of Northeastern University,2002,23(2):123-125.(in Chinese)
[17]邓勇,施文康,朱振福.一种有效处理冲突证据的组合方法[J].红外与毫米波学报,2004,23(1):27-32.
DENG Y,SHI W K,ZHU Z F.Efficient combination approach of conflict evidenee[J].Journal of Infrared Millimeter Wave,2004,23(1):27-32.(in Chinese)
[18]梁昌勇,陈增明,黄永青,等.Dempster-Shafer合成法则悖论的一种消除方法[J].系统工程理论与实践,2005,25(3):7-12.
LIANG C Y,CHEN Z M,HUANG Y Q,et al.A method of dispelling the absurdities of dempster-sharer's rule of combination[J].Systems Engineering-Theory & Practice,2005,25(3):7-12.(in Chinese)
[19]倪龙强, 周振堂, 高社生. 粗糙集和证据理论相结合的数据挖掘方法[J]. 西北工业大学学报, 2010, 28(6): 927-931.
NI L Q,ZHOU Z T,GAO S S. A more effective data mining approach that adroitly combines rough set theory with evidence theory [J]. Journal of Northwestern Polytechnical University, 2010, 28(6): 927-931. (in Chinese)
[20]LEFEVRE E,COLOT O,VANNOORENBERGHE P.Belief function combination and conflict management[J].Information Fusion,2002,3(2):149-162.
[21]高社生,倪龙强,杨凯. 一种新的基于局部冲突分配的证据合成规则[J]. 西北工业大学学报, 2009, 27(1): 43-46.
GAO S S, NI L Q, YANG K. A new and better rule for combining sharply conflicting evidences[J]. Journal of Northwestern Polytechnical University, 2009, 27(1): 43-46. (in Chinese)
[22]TAN A H, WU W Z, TAO Y Z. A unified framework for characterizing rough sets with evidence theory in various approximation spaces[J]. Information Sciences,2018, 454/455: 144-160.
[23]LIN G P, LIANG J Y, QIAN Y H. An information fusion approach by combining multigranulation rough sets and evidence theory[J]. Information Sciences,2015,314: 184-199.
[24]YAGERRONALD R. On the Dempster-Shafer framework and new combination rules[J]. Information System, 1989, 41(2): 93-137.
[25]孙伟超,李文海,李文峰. 融合粗糙集与D-S证据理论的航空装备故障诊断[J]. 北京航空航天大学学报,2015, 41 (10): 1902-1909.
SUN W C, LI W H, LI W F. Avionic devices fault diagnosis based on fusion method of rough set and D-S theory[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(10):1902-1909. (in Chinese)
[26]索中英,程嗣怡,袁修久,等.优势决策信息系统规则获取方法及应用[J].兵工学报,2015,36(3): 539-544.
SUO Z Y,CHENG S Y,YUAN X J,et al.Rule acquisition me-thodand application of dominance decision-making information system[J].Acta Armamentarii, 2015, 36(3): 539-544. (in Chinese)
[27]贾宝柱, 贾志涛, 赵祥. 基于信息融合的船舶中央冷却系统运行状态评估[J]. 大连海事大学学报,2017, 43(4): 89-96.
JIA B Z, JIA Z T, ZHAO X. Operating condition evaluation of ship central cooling system based on information fusion[J]. Journal of Dalian Maritime University,2017, 43(4): 89-96. (in Chinese)
[28]孙全, 叶秀清, 顾伟康. 一种新的基于证据理论的合成公式[J]. 电子学报, 2000, 28(8): 117-119.
SUN Q, YE X Q, GU W K. A new combination rules of evidence theory[J]. Acta Electronica Sinica, 2000, 28(8): 117-119. (in Chinese)





第40卷
第12期2019年12月兵工学报ACTA
ARMAMENTARIIVol.40No.12Dec.2019

497

Accesses

0

Citation

Detail

段落导航
相关文章

/