不确定信息下考虑相关性与多样性的作战方案推荐方法

doi:10.12305/j.issn.1001-506X.2022.10.15

Abstract

Abstract:

There are uncertainty information, experience accumulation and reuse in operation plan design, and the trade-off between relativity and diversity in operation plan recommendation. An operational plan recommendation method considering relativity and diversity under uncertain information is proposed. Firstly, the rough set theory is integrated into the best-worst method (BWM) to weight each combat capability attribute. Secondly, the relativity between operational plan and mission requirements under uncertain information and the diversity calculation model between operational plan are given. Then, a determinant point process (DPP) model is proposed to balance relativity and diversity. On this basis, greedy maximum a posterior (MAP) inference algorithm and greedy Trade-off inference algorithm are given to obtain the best recommended subset of operational plan. Finally, the applicability and feasibility of the proposed model and method are verified by case study.

Key words: combat plans recommendation, uncertain information, relativity, diversity, determinantal point process

CLC Number:

E917

Renjie XU, Lin GONG, Mingren ZHU, Jian XIE, Jingjia YU. Combat plans recommendation method considering relativity and diversity under uncertain information[J]. Systems Engineering and Electronics, 2022, 44(10): 3115-3123.

Figures/Tables 7

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Table 1

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Fig.5

References 33

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能力属性	RZ_B	RZ_W	W_s
侦察能力	[4.480, 4.840]	[5.747, 6.640]	0.136
信息处理能力	[2.650, 3.350]	[5.160, 5.640]	0.158
信息传输能力	[1.000, 1.000]	[9.000, 9.000]	0.348
辅助决策能力	[5.360, 6.253]	[4.650, 5.350]	0.100
突发应变能力	[5.560, 6.350]	[3.747, 4.640]	0.077
作战适宜性	[9.000, 9.000]	[1.000, 1.000]	0.030
打击精度	[5.360, 5.840]	[3.747, 4.640]	0.080
机动速度	[5.747, 7.080]	[3.747, 4.640]	0.071

方案	P¹	P²	P³	…	P⁵⁰
P¹	1.000	0.509	0.461	…	0.531
P²	0.509	1.000	0.427	…	0.584
P³	0.461	0.427	1.000	…	0.523
⋮	⋮	⋮	⋮	⋮	⋮
P⁵⁰	0.531	0.584	0.523	…	1
P^c	0.631	0.635	0.794	…	0.860

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Combat plans recommendation method considering relativity and diversity under uncertain information

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