基于直觉模糊折扣算子的异类数据融合方法

doi:10.12305/j.issn.1001-506X.2021.02.04

摘要/Abstract

摘要：

异类数据作为不确定数据的类型之一,成为模式识别、多属性决策等问题中数据融合的难点。为此,以直觉模糊集为理论基础,本文提出一种异类数据的多属性融合方法。首先,定义一种新的直觉模糊数折扣算子及合成法则,证明了该算子与合成法则的基本性质。其次,定义异类数据之间的距离度量测度,基于灰关联方法,得到用于决策的系数矩阵,通过对备选方案证据的构造过程,生成直觉模糊数,基于直觉模糊熵,给出属性权重的计算方法。最后,根据直觉模糊数合成法则和比较法则,实现结果判定。仿真实验结果表明,所提方法能够有效地降低运算时间,得到正确的决策结果。

关键词: 异类数据, 直觉模糊数, 折扣算子, 合成法则, 数据融合

Abstract:

As one type of uncertain data, the heterogeneous data becomes the difficulty of data fusion applied in pattern recognition, multi-attribute decision-making and other issues. Therefore, based on the intuitionistic fuzzy set theory, a multi-attribute fusion method based on heterogeneous data is proposed. A new discounting operator of intuitionistic fuzzy value and its composition rule are defined, and its basic properties are proved. Secondly, the measures of the distance among the heterogeneous datas are defined. Based on the gray correlation method, the coefficient matrix for decision-making is obtained. Through the construction process of alternative scheme evidence, the intuitionistic fuzzy values are generated. Based on the intuitionistic fuzzy entropy, the calculation method of attribute weight is given. Finally, according to the combination rule and comparison rule of the intuitionistic fuzzy value, the result judgment is realized. The simulation experiment results show that the proposed method can effectively reduce the operation time and get the correct decision results.

Key words: heterogeneous data, intuitionistic fuzzy value, discouting operator, composition rule, data fusion

中图分类号:

TP391
TN957

李双明, 关欣, 刘傲. 基于直觉模糊折扣算子的异类数据融合方法[J]. 系统工程与电子技术, 2021, 43(2): 311-317.

Shuangming LI, Xin GUAN, Ao LIU. Heterogeneous data fusion method based on intuitionistic fuzzy discout operator[J]. Systems Engineering and Electronics, 2021, 43(2): 311-317.

图/表 5

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方法	备选方案集	排序结果	运行时间/s
BF-TOPSIS1	{A₁, A₂, A₃}	A₂＞A₃＞A₁	0.133 420
BF-TOPSIS2		A₂＞A₃＞A₁	0.126 211
BF-TOPSIS3		A₃＞A₂＞A₁	0.234 185
本文方法		A₂＞A₃＞A₁	0.013 432
BF-TOPSIS1	{A₁, A₂, A₃, A₄}	A₃＞A₂＞A₄＞A₁	0.324 502
BF-TOPSIS2		A₃＞A₂＞A₄＞A₁	0.294 024
BF-TOPSIS3		A₃＞A₂＞A₄＞A₁	0.398 240
本文方法		A₃＞A₂＞A₄＞A₁	0.013 487
BF-TOPSIS1	{A₁, A₂, A₃, A₄, A₅}	A₃＞A₂＞A₄＞A₁＞A₅	0.679 371
BF-TOPSIS2		A₃＞A₂＞A₄＞A₁＞A₅	0.876 924
BF-TOPSIS3		A₃＞A₂＞A₄＞A₁＞A₅	0.928 025
本文方法		A₃＞A₂＞A₄＞A₁＞A₅	0.013 625

类别	特征属性
类别	RF/MHz	PRI/μs	PW/μs	CR
R₁	[4 940, 5 160]	[3 680, 3 750]	[0.6, 1.2]	[0.380 0, 0.404 1]
R₂	[5 420, 5 520]	[3 600, 3 680]	[0.2, 0.5]	[0.662 6, 0.673 1]
R₃	[5 100, 5 420]	[3 580, 3 650]	[1.6, 2.0]	[0.162 2, 0.229 4]
R₄	[5 160, 5 220]	[3 730, 3 800]	[0.9, 1.4]	[0.658 7, 0.698 1]
R₅	[5 520, 5 620]	[3 450, 3 550]	[1.2, 1.5]	[0.777 6, 0.809 8]

序号	RF/MHz	PRI/μs	PW/μs	Cr
工作模式67	[5.441 4, 4 632]	{3 641.8, 3 650.2, 3 657.2, 3 680.6, 3 586.5, 3 654.7}	[1.199 1, 1.200 7]	0.664 4
工作模式66	[5.441 4, 4 632]	{3 641.8, 3 650.2, 3 657.2, 3 680.6, 3 586.5, 3 654.7}	[1.199 1, 1.200 7]	0.664 3
待识别模式	{5 436.3, 5 434.3, 5 444.6, 5 435.1, 5 451.3, 5 436.5}	{3 628.1, 3 639.7, 3 662.7, 3 670.9, 3 568.5, 3 654.6}	1.214	[0.664 0, 6 663]

方法	目标1			目标2			目标3
方法	本文权重	权重1	权重2	本文权重	权重1	权重2	本文权重	权重1	权重2
BF-TOPSIS1	93.9/34.1	90.9/35.6	62.6/21.9	85.7/26.9	81.4/26.8	47.6/14.3	91.5/33.3	89.1/33	54.7/18.4
BF-TOPSIS2	90.5/29.3	91.1/34.2	64.1/22.7	85.5/24.3	80.9/25.8	46.6/13.3	88.7/32.5	89.9/35.2	54.1/20
BF-TOPSIS3	93.4/34.2	93.4/32.7	93/34.6	85.3/23.9	83.6/27	84.2/26.7	90.4/32.1	88.3/32.4	90.4/34.2
本文方法	100/83.8	100/80.2	98.4/64.8	100/81.5	100/79.2	98.7/63.7	100/81.6	100/81.1	98.4/64.2

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