Reinforcement Model for Unmanned Combat System of Systems Based Multi-Layer Grey Target

被引：0

作者：

Hao, Xueting ^{[1
,2
]}

Fang, Zhigeng ^{[1
,2
]}

Zhang, Jingru ^{[1
,2
]}

Deng, Fei ^{[3
]}

Jiang, Ankang ^{[1
,2
]}

Xiao, Shuyu ^{[1
,2
]}

机构：

[1] Nanjing Univ Aeronaut & Astron, Coll Econ & Management, Nanjing 211106, Jiangsu, Peoples R China

[2] Nanjing Univ Aeronaut & Astronaut, Inst Grey Syst Studies, Nanjing 211106, Jiangsu, Peoples R China

[3] Shanghai Yuda Ind Co Ltd, 251 Changning Rd, Shanghai 200240, Peoples R China

来源：

JOURNAL OF GREY SYSTEM | 2024年 / 36卷 / 02期

基金：

中国国家自然科学基金;

关键词：

Unmanned combat system of systems(UCSoS); Multi-layer grey target; Reinforcement model; The Agent technology; GERT technique;

D O I：

暂无

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

In the future battlefield, unmanned combat mode will be crucial. Its attack strategy formulation is a significant and complex task. To address the issue of decision-making in unmanned combat system of systems(UCSoS) striking strategy, this paper begins by analyzing the characteristics of UCSoS. By utilizing the GERT concept, an unmanned combat A-GERT network is developed to provide parameter support for evaluating its effectiveness. Secondly, for effectiveness evaluation,a multi-layer gray target model built on the A-GERT network is proposed. This model is utilized to formulate optimal striking scheme for the UCSoS. And Agent technology is employed to solve the intelligent learning decision-making problem for UCSoS based on multi-layer grey target model. Finally, a case study illustrates the efficiency and effectiveness of the reinforcement model for UCSoS based on multi-layer grey target.

引用

页数：109

共 50 条

[21] Reinforcement Learning Based Capacity Management in Multi-Layer Satellite Networks
Jiang, Chunxiao
Zhu, Xiangming
IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS, 2020, 19 (07) : 4685 - 4699
[22] UAV Path Planning Based on Multi-Layer Reinforcement Learning Technique
Cui, Zhengyang
Wang, Yong
IEEE ACCESS, 2021, 9 : 59486 - 59497
[23] Multi-layer Map Construction for Outdoor Unmanned Vehicles Based On Graph Optimization
Niu G.
Feng N.
Wang Y.
Qiche Gongcheng/Automotive Engineering, 2022, 44 (02): : 199 - 207
[24] A Multi-Layer control architecture for unmanned aerial vehicles
Boskovic, JD
Prasanth, R
Mehra, RK
PROCEEDINGS OF THE 2002 AMERICAN CONTROL CONFERENCE, VOLS 1-6, 2002, 1-6 : 1825 - 1830
[25] L CURRENT SYSTEM IN A MULTI-LAYER IONOSPHERIC MODEL
XU, WY
TSCHU, KK
MATSUSHITA, S
ACTA GEOPHYSICA SINICA, 1983, 26 (06): : 503 - 514
[26] Agent-based multi-layer, collision avoidance to unmanned aerial vehicles
Sislak, David
Volf, Pfemysl
Komenda, Antonin
Samek, Jiri
Pechoucek, Michal
2007 INTERNATIONAL CONFERENCE ON INTEGRATION OF KNOWLEDGE INTENSIVE MULTI-AGENT SYSTEMS, 2007, : 365 - +
[27] A tumour perception system based on a multi-layer mass-spring model
Zhang, Xiaorui
Duan, Jiali
Sun, Wei
Jha, Sunil Kumar
INTERNATIONAL JOURNAL OF SENSOR NETWORKS, 2019, 31 (01) : 24 - 32
[28] Construction of Sports Management System Based on Multi-Layer Neural Network Model
Wen, Jiaqi
Liang, Dong
APPLIED MATHEMATICS AND NONLINEAR SCIENCES, 2023,
[29] EXCITONS IN MULTI-LAYER SYSTEMS
FOMIN, VM
POKATILOV, EP
PHYSICA STATUS SOLIDI B-BASIC RESEARCH, 1985, 129 (01): : 203 - 209
[30] Multi-layer preceptron based modelling of nonlinear systems
Fuzzy Sets Syst, 1 (93):

← 1 2 3 4 5 →