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Liu J S, Jiang W Z, Lei Y Y, et al. Threat evaluation of air-targets for key positions air-defence under dynamic fire access [J]. Journal of Beijing University of Aeronautics and Astronautics, 2016, 42(7): 1422–1431.LiuJ SJiangW ZLeiY YThreat evaluation of air-targets for key positions air-defence under dynamic fire access[J]201642714221431Search in Google Scholar
Sun Y K, Fang Z G, Chen D, et al. Threat assessment model based on dynamic grey principal component analysis and multi-time fusion [J]. Systems Engineering and Electronics, 2020, 9.SunY KFangZ GChenDThreat assessment model based on dynamic grey principal component analysis and multi-time fusion[J]20209Search in Google Scholar
Fan C L, Xing Q H, Fu Q, et al. Modeling and realization of dynamic firepower allocation problem based on fuzzy chance constrained bilevel programming [J]. Systems Engineering – Theory & Practice, 2016, 36(5): 1318–1330.FanC LXingQ HFuQModeling and realization of dynamic firepower allocation problem based on fuzzy chance constrained bilevel programming[J]201636513181330Search in Google Scholar
Premarathne U S, Rajasingham S. Trust based multi-agent cooperative load balancing system (TCLBS) [J]. Future Generation Computer Systems, 2020, 112: 185–192.PremarathneU SRajasinghamSTrust based multi-agent cooperative load balancing system (TCLBS)[J]2020112185192Search in Google Scholar
Li Y F, Yang B, Yan L, et al. Retraction notice to “Energy-aware resource management for uplink non-orthogonal multiple access: multi-agent deep reinforcement learning” [J]. Future Generation Computer Systems, 2020, 111: 940–944.LiY FYangBYanLRetraction notice to “Energy-aware resource management for uplink non-orthogonal multiple access: multi-agent deep reinforcement learning”[J]2020111940944Search in Google Scholar
Lv Y W, Yang G H, Shi C X. Differentially private distributed optimization for multi-agent systems via the augmented Lagrangian algorithm [J]. Information Sciences, 2020, 538: 39–53.LvY WYangG HShiC XDifferentially private distributed optimization for multi-agent systems via the augmented Lagrangian algorithm[J]20205383953Search in Google Scholar
Li Y, Wang C L, Liang D Y. Truncated prediction-based distributed consensus control of linear multi-agent systems with discontinuous communication and input delay [J]. Neuro Computing, 2020, 409: 217–230.LiYWangC LLiangD YTruncated prediction-based distributed consensus control of linear multi-agent systems with discontinuous communication and input delay[J]2020409217230Search in Google Scholar
Li P, Zhao Y Y, Hu J P, et al. Distributed initialization-free algorithms for multi-agent optimization problems with coupled inequality constraints [J]. Neuro Computing, 2020, 407: 155–162.LiPZhaoY YHuJ PDistributed initialization-free algorithms for multi-agent optimization problems with coupled inequality constraints[J]2020407155162Search in Google Scholar
Zhang X X, Liu X P, Sun Y S. Bipartite output consensus for heterogeneous multi-agent systems via reduced-order observer-based protocols [J]. Neuro Computing, 2020, 406: 274–281.ZhangX XLiuX PSunY SBipartite output consensus for heterogeneous multi-agent systems via reduced-order observer-based protocols[J]2020406274281Search in Google Scholar
Xu C J, Xu H C, Su H S, et al. Disturbance-observer based consensus of linear multi-agent systems with exogenous disturbance under intermittent communication [J]. Neuro Computing, 2020, 404: 26–33.XuC JXuH CSuH SDisturbance-observer based consensus of linear multi-agent systems with exogenous disturbance under intermittent communication[J]20204042633Search in Google Scholar
Zhang L L, Chen B, Lin C. Adaptive neural consensus tracking control for a class of 2-order multi-agent systems with nonlinear dynamics [J]. Neuro Computing, 2020, 404: 84–92.ZhangL LChenBLinCAdaptive neural consensus tracking control for a class of 2-order multi-agent systems with nonlinear dynamics[J]20204048492Search in Google Scholar
Liu Y F, Li T S, Shan Q H, et al. Online optimal consensus control of unknown linear multi-agent systems via time-based adaptive dynamic programming [J]. Neuro Computing, 2020, 404: 137–144.LiuY FLiT SShanQ HOnline optimal consensus control of unknown linear multi-agent systems via time-based adaptive dynamic programming[J]2020404137144Search in Google Scholar
Xu Z Q, Li C D, Han Y Y. Impulsive consensus of nonlinear multi-agent systems via edge event-triggered control [J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(6): 1995–2004.XuZ QLiC DHanY YImpulsive consensus of nonlinear multi-agent systems via edge event-triggered control[J]202031619952004Search in Google Scholar
Zahoor A, Muhammad M K, Muhammad A S, et al. Consensus control of multi-agent systems with input and communication delay: a frequency domain perspective [J]. ISA Transactions, 2020, 101: 69–77.ZahoorAMuhammadM KMuhammadA SConsensus control of multi-agent systems with input and communication delay: a frequency domain perspective[J]20201016977Search in Google Scholar
Alexandros F, Alexandros N, Dimos V. Robust decentralised navigation of multi-agent systems with collision avoidance and connectivity maintenance using model predictive controllers [J]. International Journal of Control, 2020, 93(6): 1470–1484.AlexandrosFAlexandrosNDimosVRobust decentralised navigation of multi-agent systems with collision avoidance and connectivity maintenance using model predictive controllers[J]202093614701484Search in Google Scholar
Cheng Y. Research on consensus for several kinds of opinion dynamical models [D]. Changsha: National University of Defence Technology, 2016.ChengY[D]ChangshaNational University of Defence Technology2016Search in Google Scholar
Wang R H, Xing J C, Wang P, et al. An overview on multi-agents cooperative control of unmanned ground system [J]. Journal of Dynamics and Control, 2016, 2(14): 97–108.WangR HXingJ CWangPAn overview on multi-agents cooperative control of unmanned ground system[J]201621497108Search in Google Scholar
Han Z J, Sun S B, Zhang R Y, et al. Research on modeling technology and application of multi-agent in armored force warfare simulation [J]. Fire Control & Command and Control, 2016, 41(6): 1–4, 14.HanZ JSunS BZhangR YResearch on modeling technology and application of multi-agent in armored force warfare simulation[J]20164161414Search in Google Scholar
Zhang J, Wang G, Song Y F, et al. Optimization of air defence resource deployment based on adaptive SGD-multi-agent [J]. Systems Engineering and Electronics, 2019, 41(7): 1536–1543.ZhangJWangGSongY FOptimization of air defence resource deployment based on adaptive SGD-multi-agent[J]201941715361543Search in Google Scholar
Liu X, Liu Z, Hou W S, et al. Improved MOPSO algorithm for multi-objective programming model of weapon-target assignment [J]. Systems Engineering and Electronics, 2013, 35(2): 326–330.LiuXLiuZHouW SImproved MOPSO algorithm for multi-objective programming model of weapon-target assignment[J]2013352326330Search in Google Scholar
Ma X X, Teng K N, Hou X L. Troops demand analysis of reef air defence [J]. Command Control & Simulation, 2017, 39(2): 1–4.MaX XTengK NHouX LTroops demand analysis of reef air defence[J]201739214Search in Google Scholar
Li L Y, Liu F X, Zhao L F. Direct interceptor allocation method in antimissile firepower planning for multiple wave targets [J]. Systems Engineering and Electronics, 2014, 36(11): 2206–2212.LiL YLiuF XZhaoL FDirect interceptor allocation method in antimissile firepower planning for multiple wave targets[J]2014361122062212Search in Google Scholar
Wang Y F, Sheng A D, Li Y Y, et al. Distributed networked antiaircraft fire-control system based on consensus of multi-agent systems [J]. Fire Control & Command Control, 2018, 43(11): 102–107.WangY FShengA DLiY YDistributed networked antiaircraft fire-control system based on consensus of multi-agent systems[J]20184311102107Search in Google Scholar
Wang H F, Gao X J, Liu H. Joint firepower mission planning method based on competitive leapfrog algorithm [J]. Command Control & Simulation, 2019: 1–7.WangH FGaoX JLiuHJoint firepower mission planning method based on competitive leapfrog algorithm[J]201917Search in Google Scholar
Chen W P, Ao Z G, Tu Y Q, et al. Assessment of survivability of missile position command system [J]. Journal of Weapon Equipment Engineering, 2016, 37(5): 89–92.ChenW PAoZ GTuY QAssessment of survivability of missile position command system[J]20163758992Search in Google Scholar