基于犹豫模糊集的不等长序列识别方法及应用

doi:10.11959/j.issn.1000-436x.2021118

Abstract

Abstract:

Aiming at the problem that unequal length sequences were difficult to recognize, a recognition method based on hesitant fuzzy distance measure was proposed.Firstly, the problem was described from the perspective of fuzzy value, and the hesitant fuzzy information recognition model of unequal length sequence was established by lattice closeness degree.Secondly, the mean value, variance, relative range and hesitancy degree of hesitant fuzzy values were defined.Combined with membership difference of the shorter part, the generalized integrated feature distance measure and the generalized weighted integrated feature distance measure were defined to meet relevant properties of metric space, and the strict mathematical proof process was given.Finally, entropy measure and support measure were proposed to determine the weight, and the VIKOR recognition method based on hesitant distance measure was given.The simulation results verify the effectiveness and feasibility of proposed method from numerical examples, energy strategy selection and target recognition respectively.

Key words: hesitant fuzzy set, distance measure, recognition for unequal length sequences

CLC Number:

TP391

Shuangming LI, Xin GUAN, Guidong SUN. Recognition method based on hesitant fuzzy set for unequal length sequences and its application[J]. Journal on Communications, 2021, 42(7): 41-51.

Figures/Tables 12

类别	工作模式	特征1	特征2	…	特征p
	$m_{1, 1}$	$s_{11}^{m_{1, 1}}$	$s_{12}^{m_{1, 1}}$	…	$s_{1 p}^{m_{1, 1}}$
1	?	?	?	$⋱$	?
	$m_{1, n_{1}}$	$s_{11}^{m_{1, n_{1}}}$	$s_{12}^{m_{1, n_{1}}}$	…	$s_{1 p}^{m_{1, n_{1}}}$
	$m_{2, 1}$	$s_{21}^{m_{2, 1}}$	$s_{22}^{m_{2, 1}}$	…	$s_{2 p}^{m_{2, 1}}$
2	?	?	?	$⋱$	?
	$m_{2, n_{2}}$	$s_{21}^{m_{2, n_{2}}}$	$s_{22}^{m_{2, n_{2}}}$	…	$s_{2 p}^{m_{2, n_{2}}}$
?	?	?	?	?	?
	$m_{n, 1}$	$s_{n 1}^{m_{n, 1}}$	$s_{n 2}^{m_{n, 1}}$	…	$s_{n p}^{m_{n, 1}}$
n	?	?	?	$⋱$	?
	$m_{n, n_{n}}$	$s_{n 1}^{m_{n, n_{n}}}$	$s_{n 2}^{m_{n, n_{n}}}$	…	$s_{n p}^{m_{n, n_{n}}}$

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方法	距离	d(h₀,h₁)	d(h₀,h₂)	d(h₀,h₁)<d(h₀,h₂)
	λ= 1	0.233 3	0.23 3	FALSE
	λ= 2	0.234 5	0.234 5	FALSE
文献[7]方法	λ= 3	0.235 6	0.235 6	FALSE
	λ= 6	0.238 5	0.238 5	FALSE
	λ=10	0.241 5	0.241 5	FALSE
	λ=1	0.200 0	0.116 7	FALSE
	λ= 2	0.203 4	0.165 8	FALSE
文献[9-10]方法	λ= 3	0.206 9	0.187 0	FALSE
	λ= 6	0.216 4	0.212 5	FALSE
	λ=10	0.225 7	0.225 2	FALSE
	λ= 1	0.216 7	0.175 0	FALSE
	λ= 2	0.219 5	0.203 1	FALSE
文献[11]方法	λ= 3	0.222 2	0.214 1	FALSE
	λ= 6	0.228 8	0.223 7	FALSE
	λ=10	0.234 7	0.234 5	FALSE
	λ= 1	0.129 5	0.180 0	TRUE
	λ= 2	0.155 1	0.213 2	TRUE
本文方法	λ= 3	0.168 9	0.230 3	TRUE
	λ= 6	0.187 2	0.255 4	TRUE
	λ=10	0.197 5	0.271 1	TRUE

方法	距离	d(h₁,h₂)	d(h₁,h₃)	d(h₂,h₃)	d(h₁,h₂)<d(h₁,h₃)+d(h₂,h₃)
	λ= 1	0.4221	0.3817	0.0332	FALSE
	λ= 2	0.4665	0.4067	0.0335	FALSE
文献[7]方法	λ= 3	0.4888	0.4334	0.0338	FALSE
	λ= 6	0.5555	0.4989	0.0348	FALSE
	λ=10	0.5985	0.5440	0.0359	FALSE
	λ= 1	0.3055	0.2857	0.0531	TRUE
	λ= 2	0.3398	0.3168	0.0556	TRUE
本文方法	λ= 3	0.3755	0.3497	0.0582	TRUE
	λ= 6	0.4632	0.4334	0.0652	TRUE
	λ=10	0.5292	0.4969	0.0711	TRUE

方法	距离	d(h₁,h₂)	d(h₁,h₃)	d(h₂,h₃)	d(h₁,h₂)<d(h₁,h₃)+d(h₂,h₃)
	λ= 1	0.5167	0.1833	0.2500	FALSE
	λ= 2	0.5172	0.2901	0.3536	TRUE
文献[11]方法	λ= 3	0.5178	0.3468	0.3969	TRUE
	λ= 6	0.5194	0.4163	0.4454	TRUE
	λ=10	0.5218	0.4480	0.4665	TRUE
	λ= 1	0.4000	0.1500	0.1667	FALSE
	λ= 2	0.4486	0.2525	0.2887	TRUE
文献[17]方法	λ= 3	0.4705	0.3152	0.3467	TRUE
	λ= 6	0.4951	0.3969	0.4163	TRUE
	λ=10	0.5070	0.4353	0.4480	TRUE
	λ= 1	0.3275	0.1275	0.2000	TRUE
	λ= 2	0.4075	0.2259	0.3162	TRUE
本文方法	λ= 3	0.4435	0.2926	0.3684	TRUE
	λ= 6	0.4840	0.3824	0.4292	TRUE
	λ=10	0.5022	0.4257	0.4562	TRUE

方案/准则	C₁	C₂	C₃	C₄
P₁	{0.5,0.4,0.3}	{0.9,0.8,0.7,0.1}	{0.5,0.4,0.2}	{0.9,0.6,0.5,0.3}
P₂	{0.5,0.3}	{0.9,0.7,0.6,0.5,0.2}	{0.8,0.6,0.5,0.1}	{0.7,0.3,0.4}
P₃	{0.7,0.6}	{0.9,0.6}	{0.7,0.5,0.3}	{0.6,0.4}
P₄	{0.8,0.7,0.4,0.3}	{0.7,0.4,0.2}	{0.8,0.1}	{0.9,0.8,0.6}
P₅	{0.9,0.7,0.6,0.3,0.1}	{0.8,0.7,0.6,0.4}	{0.9,0.8,0.7}	{0.9,0.7,0.6,0.3}

方法	距离	P₁	P₂	P₃	P₄	P₅	排序结果
	λ= 1	0.479 9	0.502 7	0.402 5	0.429 2	0.355 8	P ₅>P₃>P₄>P_2.>P₁
文献[7]方法	λ=2	0.537 8	0.545 1	0.436 6	0.505 2	0.412 9	P ₅>P₃>P₄>P_2.>P₁
	λ=6	0.659 9	0.647 6	0.515 6	0.670 4	0.569 9	P ₃>P₅>P₂>P₁>P₄
	λ=10	0.721 3	0.704 6	0.560 7	0.737 3	0.653 7	P ₃>P₅>P₂>P₁>P₄
	λ=1	0.477 9	0.502 7	0.402 5	0.429 2	0.355 8	P ₅>P₃>P₄>P₁>P₂
文献[13]方法	λ=2	0.537 8	0.545 1	0.436 6	0.505 2	0.412 9	P ₅>P₃>P₄>P₁>P₂
	λ= 6	0.659 9	0.647 6	0.515 6	0.670 4	0.569 9	P ₃>P₅>P₂>P₁>P₄
	λ=10	0.721 3	0.704 7	0.560 3	0.737 4	0.653 7	P ₃>P₅>P₂>P₁>P₄
	Φ₁w(with r=1)	0.240 0	0.250 0	0.275 0	0.195 0	0.130 0	P ₅>P₄>P₁>P₂>P₃
文献[16]方法	Φ₂w(with r=2)	0.780 0	0.735 0	0.530 0	0.665 0	0.620 0	P ₃>P₅>P₄>P₂>P₁
	Φ₃w(with r=3)	0.359 0	0.350 5	0.436 3	0.312 1	0.239 1	P ₅>P₄>P₂>P₁>P₃
	Φ₄w(with r=4)	0.400 2	0.480 0	0.363 3	0.320 4	0.288 5	P ₅>P₄>P₃>P₁>P₂
	λ=1	0.141 8	0.147 6	0.121 9	0.094 7	0.138 2	P ₃>P₅>P₄>P₂>P₁
本文方法	λ=2	0.237 1	0.267 5	0.224 5	0.200 1	0.267 9	P ₃>P₅>P₄>P₂>P₁
	λ=6	0.490 8	0.448 1	0.367 9	0.428 2	0.489 5	P ₃>P₄>P₅>P₂>P₁
	λ=10	0.576 1	0.516 5	0.412 9	0.511 0	0.575 9	P ₃>P₄>P₂>P₅>P₁

Recognition method based on hesitant fuzzy set for unequal length sequences and its application

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目标	模式		特征参数
目标	模式	A₁/MHz	A₂/μs	A₃/μs
	1	1 630, 1 695, 1 750	830, 880, 930	6.5, 7.2, 9.8
U₁	2	3 910, 3 973	980, 1 030	6.1, 6.8, 7.5
	3	3 362, 3 448, 3 510	990	4.5, 5.6
U₂	4	3 440	337, 387, 440	20.3, 21.2
	5	3 810, 3 884, 3 940	1 300, 1 362	6.9
U₃	6	1 580, 1 640	2 762, 2 825, 2 896	11.2, 12.3, 16.9
	7	5 456, 5 610, 5 684	2 735, 2 795, 2 855	7.8, 8.6
	8	1 486, 1 541, 1 601	1 630, 1 701, 1 785	1.2, 1.8, 2.6
U₄	9	1 337	1 600, 1 685	1.9, 2.8, 3.5
	10	4 381, 4 426	785	3.1, 3.8

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