用于异质信息的信任区间交互式多属性识别方法

李双明; 关欣; 衣晓; 吴斌

doi:10.11999/JEIT200038

用于异质信息的信任区间交互式多属性识别方法

doi: 10.11999/JEIT200038

李双明^{1, 2, ,},
关欣¹,
衣晓¹,
吴斌¹

1.
海军航空大学烟台 264001
2.
92941部队葫芦岛 125001

基金项目: 国防科技卓越青年科学基金(2017-JCJQ-ZQ-003)，泰山学者工程专项经费(ts 201712072)

详细信息

作者简介:
李双明：男，1986年生，博士，主要研究方向为目标识别技术

关欣：女，1978年生，博士，教授，主要研究方向为信息融合、电子对抗及智能计算

衣晓：男，1975年生，博士，教授，主要研究方向为无线传感器网络，多源信息融合

吴斌：男，1992年生，博士，主要研究方向为主要研究方向为无线传感器网络

通讯作者:
李双明　aminglishuang@126.com

中图分类号: TN911.7; TN957
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- 被引次数: 0
出版历程
- 收稿日期: 2020-01-09
- 修回日期: 2020-10-23
- 网络出版日期: 2020-12-07
- 刊出日期: 2021-05-18

A BI-TODIM Approach Used for Heterogeneous Information Fusion

Shuangming LI^{1, 2
, ,},
Xin GUAN¹,
Xiao YI¹,
Bin WU¹

1.
Naval Aviation University, Yantai 264001, China
2.
PLA Unit 92941, Huludao 125001, China

Funds: The National Defense Science and Technology Excellence Youth Talent Fund (2017-JCJQ-ZQ-003), The Taishan Scholar Engineering Special Fund (ts 201712072)

摘要

摘要: 为了解决混合类型数据与专家知识等异质信息的融合决策问题，该文提出了基于信任区间的交互式多属性识别(BI-TODIM)方法。完善了混合类型数据的距离测度，根据信任区间的构建定理和灰关联方法构建了未知目标混合类型数据的信任区间，阐明了信任区间与直觉模糊数之间的等价关系，创建了混合类型数据和专家知识的识别决策模型，实现了特征层信息和决策层信息的统一表达；分析了基于信度函数的逼近理想解(BF-TOPSIS)方法的反转现象及算法的复杂度，定义了区间数的序关系，提出了BI-TODIM识别决策方法，及基于直觉模糊熵的未知权重计算方法。结合算例和目标识别案例，验证了该文方法在解决排序反转和异质信息融合方面的有效性，突出了该方法时间复杂度低、稳定性好、识别准确度高的优点。
- 信任区间 /
- 交互式多属性 /
- 异质信息 /
- 距离测度 /
- 关联系数
Abstract: A the Interative Multi-criteria Decision making based on Belief Interval (BI-TODIM) approach is proposed to solve the fusion decision problem of heterogeneous information with mixed type data and expert knowledge. According to the construction theorem of trust interval and grey relation method, the trust interval of mixed type data of unknown target is constructed. The equivalence relationship between trust interval and intuitionistic fuzzy number is clarified. The recognition decision model of mixed type data and expert knowledge is established. The unified expression of feature layer information and decision layer information is realized. The shortcomings of the Technique for Order Preference by Similarity to Ideal Solution based on Belief Function (BF-TOPSIS) method are analyzed such as the inversion phenomenon and the complexity. To solve this problem, the order relation of interval numbers is defined, the BI-TODIM recognition decision method and the method of calculating unknown weight based on intuitionistic fuzzy entropy are proposed. The effectiveness of the proposed method in resolving ranking inversion and heterogeneous information fusion is verified by an example and a target identification case, which underlines low time complexity, good stability and high recognition accuracy.
- Belief interval /
- Interative multi-criteria /
- Heterogeneous information /
- Distance measure /
- Correlation coefficient

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图 1 算法流程示意图

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图 2 直觉模糊信息的图形表示

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图 3 信任区间的图形表示

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图 4 未知目标1/2/3的前景值变化曲线

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表 1 本文方法及BF-TOPSIS1/2的计算结果

排序方法	备选方案集	排序结果	运行时间(s)
本文方法	{A₁, A₂, A₃, A₄}	A₁$ \succ $A₂$ \succ $A₄$ \succ $A₃	0.003306
BF-TOPSIS1		A₁$ \succ $A₂$ \succ $A₄$ \succ $A₃	0.663752
BF-TOPSIS2		A₁$ \succ $A₂$ \succ $A₄$ \succ $A₃	0.789059
BF-TOPSIS3		A₂$ \succ $A₁$ \succ $A₄$ \succ $A₃	21.255864

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表 2 分类结果比较(%)

分类方法	数据集
分类方法	Iris	Wine	Glass
KNN	95.33	72.47	74.29
LST-KSVC	99.27	94.27	65.76
FGGCA	97.22	97.10	93.65
WLTSVM	98.00	96.40	49.91
本文方案	97.10	96.20	94.70

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表 3 工作模式的数据取值范围

类别	特征参数
类别	RF(MHz)	PRI(μs)	PW(μs)	Cr
R₁	[4940, 5160]	[3680, 3750]	[0.6, 1.2]	[0.3800, 0.4041]
R₂	[5420, 5520]	[3600, 3680]	[0.2, 0.5]	[0.6626, 0.6731]
R₃	[5100, 5420]	[3580, 3650]	[1.6, 2.0]	[0.1622, 0.2294]
R₄	[5160, 5220]	[3730, 3800]	[0.9, 1.4]	[0.6587, 0.6981]
R₅	[5520, 5620]	[3450, 3550]	[1.2, 1.5]	[0.7776, 0.8098]

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表 4 参数1下识别结果的正确率(%)

目标	本文方法			BF-TOPSIS1方法
目标	本文权重	权重1	权重2	本文权重	权重1	权重2
1	97.3	91.8	60.6	87.2	86.0	33.6
2	94.4	86.8	38.0	79.6	79.6	27.9
3	97.1	92.3	52.4	88.7	87.8	33.2

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