模糊认知图研究进展及其在无人系统中的应用

doi:10.12382/bgxb.2024.0804

摘要/Abstract

摘要：

模糊认知图作为一种兼具模糊推理和类神经网络特征的知识图解软计算模型,与第3代人工智能知识与数据双驱动的发展方向高度契合,已被广泛应用于各个领域。首先,介绍模糊认知图理论的基本概念和原理,从构建方法、学习算法、拓展模型3个方面系统地分析模糊认知图的最新研究进展和存在的主要问题,总结未来研究的主要方向和重点内容;其次,全面梳理和归纳模糊认知图在无人系统(单无人系统、多无人系统、无人-有人系统)中的应用研究情况;最后,通过详细分析自主智能驱动的单无人系统、群体智能驱动的多无人系统以及互信智能驱动的无人-有人系统的技术需求,深入讨论未来模糊认知图在无人系统应用中的重点研究内容和主要研究思路。

关键词: 模糊认知图, 人工智能, 知识与数据驱动, 无人系统

Abstract:

Fuzzy cognitive map (FCM),as a kind of knowledge graphical soft computing model with both fuzzy reasoning and neural network-like features,is highly compatible with the development direction of the third generation of artificial intelligence driven by both knowledge and data,and has been widely used in various fields.Firstly,the basic concepts and principles of FCM theory are introduced,the latest research progress and major existing problems of FCM are systematically analyzed from the three aspects of construction method,learning algorithm and extension model,and the main directions and key contents of the future research are summarized.Secondly,the application research of FCM in unmanned systems (single unmanned systems,multi-unmanned systems,unmanned-manned systems) are comprehensively reviewed and summarized.Finally,the key research contents and main research ideas of FCM in unmanned systems applications in the future are discussed in depth by analyzing in detail the technical requirements of single unmanned systems driven by autonomous intelligence,multi-unmanned systems driven by swarm intelligence,and unmanned-manned systems driven by mutual trust intelligence.

Key words: fuzzy cognitive map, artificial intelligence, knowledge-data-driven, unmanned systems

中图分类号:

TP18
E91

陈军, 佟龑, 牛轶峰, 于宏渤, 李霓, 张馨予. 模糊认知图研究进展及其在无人系统中的应用[J]. 兵工学报, 2025, 46(7): 240804-.

CHEN Jun, TONG Yan, NIU Yifeng, YU Hongbo, LI Ni, ZHANG Xinyu. A Review on Fuzzy Cognitive Map and Its Applications in Unmanned Systems[J]. Acta Armamentarii, 2025, 46(7): 240804-.

图/表 14

图1 近15年模糊认知图领域文献发表情况

Fig.1 FCM papers published in the last 15 years

图2 主要研究内容

Fig.2 Main research content

图3 模糊认知图基本模型

Fig.3 Basic model of FCM

图4 引入记忆的模糊认知图模型

Fig.4 FCM model with memory

图5 引入时延的模糊认知图模型

Fig.5 FCM model with delay

图6 基于知识的模糊认知图构建方法

Fig.6 Knowledge-based approach for FCM construction

图7 基于数据的模糊认知图构建方法

Fig.7 Data-based approach for FCM construction

图8 模糊认知图主要学习算法

Fig.8 Learning algorithms of FCM

图9 模糊认知图拓展模型谱系图

Fig.9 Spectral map of FCM extensions

图10 模糊认知图拓展模型的分类图

Fig.10 Classification diagram of FCM extensions

图11 无人系统及其智能技术领域

Fig.11 Unmanned systems and their intelligent technology fields

图12 基于自主智能的单无人系统应用研究

Fig.12 Applied research on single unmanned systems based on autonomous intelligence

图13 基于群体智能的多无人系统应用研究

Fig.13 Applied research on multi-unmanned systems based on swarm intelligence

图14 基于互信智能的无人-有人系统应用研究

Fig.14 Applied research on unmanned-manned systems based on mutual trust intelligence

参考文献 44

[45]	YESIL E, OZTURK C, DODURKA M F, et al. Fuzzy cognitive maps learning using artificial bee colony optimization[C]// Proceedings of the 2013 IEEE International Conference on Fuzzy Systems.Hyderabad, India:IEEE,2013:1-8.
[46]	AHMADI S, FOROUZIDEH N, ALIZADEH S, et al. Learning fuzzy cognitive maps using imperialist competitive algorithm[J]. Neural Computing and Applications, 2015, 26(6):1333-1354.
[47]	PAPAGEORGIOU E I, GROUMPOS P P. A new hybrid method using evolutionary algorithms to train fuzzy cognitive maps[J]. Applied Soft Computing, 2005, 5(4):409-431.
[48]	ZHU Y C, ZHANG W. An integrated framework for learning fuzzy cognitive map using RCGA and NHL algorithm[C]// Proceedings of the 4th International Conference on Wireless Communications,Networking and Mobile Computing.Dalian, China:IEEE,2008:1-5.
[49]	NATARAJAN R, SUBRAMANIAN J, PAPAGEORGIOU E I. Hybrid learning of fuzzy cognitive maps for sugarcane yield classification[J]. Computers and Electronics in Agriculture, 2016,127:147-157.
[50]	LIU X D, ZHANG Y L. Numerical dynamic modeling and data driven control via least square techniques and Hebbian learning algorithm[J]. International Journal of Numerical Analysis & Modeling, 2010, 7(1):66-86.
[51]	马楠, 杨炳儒, 邱正强, 等. 基于测度递进的模糊认知图及其应用[J]. 计算机工程与设计, 2012, 33(5):1958-1962.
	MA N, YANG B R, QIU Z Q, et al. Progressive measure based fuzzy cognitive map and its application[J]. Computer Engineering and Design, 2012, 33(5):1958-1962. (in Chinese)
[52]	REN Z. Learning fuzzy cognitive maps by a hybrid method using nonlinear Hebbian learning and extended great deluge algorithm[C]// Proceedings of the 23rd Midwest Artificial Intelligence and Cognitive Science Conference.Cincinnati,OH, US:Elsevier,2012:159-163.
[53]	STACH W, KURGAN L, PEDRYCZ W. A divide and conquer method for learning large fuzzy cognitive maps[J]. Fuzzy Sets and Systems, 2010, 161(19):2515-2532.
[54]	彭珍, 田立勤, 吴静, 等. 基于大型模糊认知图的复杂系统建模与推理研究[J]. 计算机科学, 2013, 40(6):203-205,210.
	PENG Z, TIAN L Q, WU J, et al. Research on complex system modeling and reasoning based on large fuzzy cognitive map[J]. Computer Science, 2013, 40(6):203-205,210. (in Chinese)
[55]	CHI Y X, LIU J. Reconstructing gene regulatory networks with a memetic-neural hybrid based on fuzzy cognitive maps[J]. Natural Computing, 2019, 18(2):301-312.
[56]	HATWAGNER M F, KOCZY L T. Parameterization and concept optimization of FCM models[C]// Proceedings of the 2015 IEEE International Conference on Fuzzy Systems.Istanbul, Turkey:IEEE,2015:1-8.
[57]	HATWAGNER M F, YESIL E, DODURKA M F, et al. Two-stage learning based fuzzy cognitive maps reduction approach[J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5):2938-2952.
[58]	PAPAGEORGIOU E I, HATWAGNER M F, BURUZS A, et al. A concept reduction approach for fuzzy cognitive map models in decision making and management[J]. Neurocomputing, 2017,232:16-33.
[59]	HOMENDA W, JASTRZEBSKA A, PEDRYCZ W. Time series modeling with fuzzy cognitive maps:simplification strategies:the case of a posteriori removal of nodes and weights[C]// Proceedings of the 13rd IFIP International Conference on Computer Information Systems and Industrial Management.Ho Chi Minh, Vietnam:Springer,2014:409-420.
[60]	邹旭苗. 基于信息理论的大规模模糊认知图学习算法及其应用研究[D]. 西安: 西安电子科技大学, 2018.
	ZOU X M. Information theory based learning algorithms and applications for large-scale fuzzy cognitive map[D]. Xi’an: Xidian University, 2018. (in Chinese)
[61]	CHEN Y, MAZLACK L J, MINAI A A, et al. Inferring causal networks using fuzzy cognitive maps and evolutionary algorithms with application to gene regulatory network reconstruction[J]. Applied Soft Computing, 2015,37:667-679.
[62]	FENG G L, LU W, PEDRYCZ W, et al. The learning of fuzzy cognitive maps with noisy data:a rapid and robust learning method with maximum entropy[J]. IEEE Transactions on Cybernetics, 2019, 51(4):2080-2092.
[63]	LU W, FENG G L, LIU X D, et al. Fast and effective learning for fuzzy cognitive maps:a method based on solving constrained convex optimization problems[J]. IEEE Transactions on Fuzzy Systems, 2020, 28(11):2958-2971.
[64]	YANG Z, LIU J. Learning of fuzzy cognitive maps using a niching-based multi-modal multi-agent genetic algorithm[J]. Applied Soft Computing, 2019,74:356-367.
[65]	沈芳. 面向时间序列的复杂系统重建算法及其应用研究[D]. 西安: 西安电子科技大学, 2021.
	SHEN F. Complex system reconstruction algorithm based on time series and its application[D]. Xi’an: Xidian University, 2021. (in Chinese)
[66]	CARVALHO J P, TOME J B A. Rule based fuzzy cognitive maps-expressing time in qualitative system dynamics[C] // Proceedings of the 10th IEEE International Conference on Fuzzy Systems. Melbourne, Australia:IEEE,2001:280-283.
[67]	SONG H J, MIAO C Y, WUYTS R, et al. An extension to fuzzy cognitive maps for classification and prediction[J]. IEEE Transactions on Fuzzy Systems, 2010, 19(1):116-135.
[68]	吕镇邦, 周利华. 混合模糊认知图[J]. 西安电子科技大学学报, 2007, 34(5):779-783.
	LÜ Z B, ZHOU L H. Hybrid fuzzy cognitive maps[J]. Journal of Xidian University, 2007, 34(5):779-783. (in Chinese)
[69]	SALMERON J L. Modelling grey uncertainty with fuzzy grey cognitive maps[J]. Expert Systems with Applications, 2010, 37(12):7581-7588.
[70]	CHEN J, GAO X D, RONG J. Enhance the uncertainty modeling ability of fuzzy grey cognitive maps by general grey number[J]. IEEE Access, 2020,8:163844-163856.
[71]	IAKOVIDIS D K, PAPAGEORGIOU E I. Intuitionistic fuzzy cognitive maps for medical decision making[J]. IEEE Transactions on Information Technology in Biomedicine, 2010, 15(1):100-107.
[72]	ZHANG Y J, QIN J H, SHI P, et al. High-order intuitionistic fuzzy cognitive map based on evidential reasoning theory[J]. IEEE Transactions on Fuzzy Systems, 2018, 27(1):16-30.
[73]	YAO X X, DING F Q, LUO C. Time series prediction based on high-order intuitionistic fuzzy cognitive maps with variational mode decomposition[J]. Soft Computing, 2022, 26(1):189-201.
[74]	骆祥峰, 高隽. 概率模糊认知图[J]. 中国科学技术大学学报, 2003, 33(1):29-36.
	LUO X F, GAO J. Probabilistic fuzzy cognitive maps[J]. Journal of University of Science and Technology of China, 2003, 33(1):29-36. (in Chinese)
[75]	潘晓勇, 骆祥峰, 刘光复, 等. 基于层次概率模糊认知图的产品拆卸序列研究[J]. 机械工程学报, 2003, 39(4):6-10.
	PAN X Y, LUO X F, LIU G F, et al. Research on disassembly sequence of products based on hierarchical probabilistic fuzzy cognitive map[J]. Chinese Journal of Mechanical Engineering, 2003, 39(4):6-10. (in Chinese)
[76]	骆祥峰, 高隽, 张旭东. 基于信任知识库的概率模糊认知图[J]. 计算机研究与发展, 2003, 40(7):925-933.
	LUO X F, GAO J, ZHANG X D. Probabilistic fuzzy cognitive map based on belief knowledge database[J]. Journal of Computer Research and Development, 2003, 40(7):925-933. (in Chinese)
[77]	SLON G, YASTREBOV A. Remarks on the uncertainty expansion problem in calculations of models of relational fuzzy cognitive maps[C]// Proceedings of the 2017 IEEE International Conference on Fuzzy Systems.Naples, Italy:IEEE,2017:1-8.
[78]	RUAN D, HARDEMAN F, MKRTCHYAN L. Using belief degree-distributed fuzzy cognitive maps in nuclear safety culture assessment[C]// Proceedings of the 2011 Annual Meeting of the North American Fuzzy Information Processing Society.El Paso,TX, US:Elsevier,2011:1-6.
[79]	HAJEK P, PROCHAZKA O. Interval-valued fuzzy cognitive maps for supporting business decisions[C]// Proceedings of the 2016 IEEE International Conference on Fuzzy Systems.Vancouver, Canada:IEEE,2016:531-536.
[80]	NAIR A, RECKIEN D, VAN MAARSEVEEN M F A M. A generalised fuzzy cognitive mapoing approach for modelling complex systems[J]. Applied Soft Computing, 2019,84:105754.
[81]	AMIRKHANI A, NASIRIYAN R H, PAPAGEORGIOU E I. A novel fuzzy inference approach:neuro-fuzzy cognitive map[J]. International Journal of Fuzzy Systems, 2020, 22(3):859-872.
[82]	MIAO Y, LIU Z Q, SIEW C K, et al. Dynamical cognitive network-an extension of fuzzy cognitive map[J]. IEEE Transactions on Fuzzy Systems, 2001, 9(5):760-770.
[83]	CHEN C T, CHIU Y T. A study of dynamic fuzzy cognitive map model with group consensus based on linguistic variables[J]. Technological Forecasting and Social Change, 2021,171:120948.
[84]	LIU B Y, FAN W H, XIAO T Y. Unsupervised dynamic fuzzy cognitive map[J]. Tsinghua Science and Technology, 2015, 20(3):285-292.
[85]	AGUILAR J. A fuzzy cognitive map based on the random neural model[C]// Proceedings of the 14th International Conference on Industrial Engineering and Other Applications of Applied Intelligent Systems.Budapest, Hungary:Springer,2001:333-338.
[86]	AGUILAR J. A dynamic fuzzy-cognitive-map approach based on random neural networks[J]. International Journal of Computational Cognition, 2003, 1(4):91-107.
[87]	KOTTAS T L, BOUTALIS Y S, CHRISTODOULOU M A. Fuzzy cognitive network:a general framework[J]. Intelligent Decision Technologies, 2007, 1(4):183-196.
[88]	WANG J Y, PENG Z, WANG X D, et al. Deep fuzzy cognitive maps for interpretable multivariate time series prediction[J]. IEEE Transactions on Fuzzy Systems, 2020, 29(9):2647-2660.
[89]	CHEN J, GAO X D, RONG J, et al. The dynamic extensions of fuzzy grey cognitive maps[J]. IEEE Access, 2021,9:98665-98678.
[90]	ZHANG N N, YAO X X, LUO C. The prediction of online time series with concept drift based on dynamic intuitionistic fuzzy cognitive map[J]. Intelligent Data Analysis, 2021, 25(4):949-972.
[91]	CAI Y D, MIAO C Y, TAN A H, et al. Creating an immersive game world with evolutionary fuzzy cognitive maps[J]. IEEE Computer Graphics and Applications, 2009, 30(2):58-70.
[92]	PARK K S, KIM S H. Fuzzy cognitive maps considering time relationships[J]. International Journal of Human-Computer Studies, 1995, 42(2):157-168.
[93]	ZHONG H M, MIAO C Y, SHEN Z Q, et al. Temporal fuzzy cognitive maps[C]// Proceedings of the 2008 IEEE International Conference on Fuzzy Systems.Hong Kong, China:IEEE,2008:1833-1833.
[94]	BOURGANI E, STYLIOS C D, MANIS G, et al. Integrated approach for developing timed fuzzy cognitive maps[J]. Advances in Intelligent Systems and Computing, 2015,322:193-204.
[95]	JIANG C B, WANG D, GONG C P, et al. Prediction of key parameters of coal gasification process based on time delay mining fuzzy time cognitive maps[C]// Proceedings of the 2021 IEEE International Conference on Real-Time Computing and Robotics.Xining, China:IEEE,2021:189-193.
[96]	STACH W, KURGAN L, PEDRYCZ W. Higher-order fuzzy cognitive maps[C]// Proceedings of the 2006 Annual Meeting of the North American Fuzzy Information Processing Society. Montreal, Canada:IEEE,2006:166-171.
[97]	薛建儒, 房建武, 吴俊, 等. 多机协同智能发展战略研究[J]. 中国工程科学, 2024, 26(1):101-116. doi: 10.15302/J-SSCAE-2024.01.013
	XUE J R, FANG J W, WU J, et al. Collaborative multiple autonomous systems[J]. Strategic Study of CAE, 2024, 26(1):101-116. (in Chinese) doi: 10.15302/J-SSCAE-2024.01.013
[98]	段海滨, 梅宇, 赵彦杰, 等. 2023年无人机热点回眸[J]. 科技导报, 2024, 42(1):217-231. doi: 10.3981/j.issn.1000-7857.2024.01.014
	DUAN H B, MEI Y, ZHAO Y J, et al. Review of technological hotspots of unmanned aerial vehicle in 2023[J]. Science & Technology Review, 2024, 42(1):217-231. (in Chinese)
[99]	苏波, 江磊, 刘宇飞, 等. 移动机器人跨域跃质关键技术综述[J]. 兵工学报, 2023, 44(9):2556-2567. doi: 10.12382/bgxb.2023.0414
	SU B, JIANG L, LIU Y F, et al. A review of key technologies for cross-domain and trans-medium of mobile robotics[J]. Acta Armamentarii, 2023, 44(9):2556-2567. (in Chinese) doi: 10.12382/bgxb.2023.0414
[100]	陈军, 徐嘉, 高晓光. 基于ABFCM模型框架的UCAV自主攻击决策[J]. 系统工程与电子技术, 2017, 39(3):549-556.
	CHEN J, XU J, GAO X G. Autonomous attack decision-making of UCAV based on ABFCM model framework[J]. Systems Engineering and Electronics, 2017, 39(3):549-556. (in Chinese) doi: 10.3969/j.issn.1001-506X.2017.03.14
[101]	CAVALIERE D, SENATORE S, LOIA V. A multi-perspective aerial monitoring system for scenario detection[C]// Proceedings of the 2018 IEEE Workshop on Environmental,Energy,and Structural Monitoring Systems.Salerno, Italy:IEEE,2018:13-18.
[102]	崔军辉, 魏瑞轩, 张小倩, 等. 无人机搜索和跟踪的模糊认知控制器设计方法[J]. 中南大学学报(自然科学版). 2015, 46(9):3279-3286.
	CUI J H, WEI R X, ZHANG X Q, et al. Design of fuzzy cognitive controllers for unmanned aerial vehicle searching and tracking purposes[J]. Journal of Central South University (Science and Technology), 2015, 46(9):3279-3286. (in Chinese)
[103]	LIU Y Y, ZHANG W, ZHANG Z L, et al. Research on behaviour planning for power substation inspection robot based on fuzzy cognitive map[C]// Proceedings of the 3rd International Conference on Advanced Materials and Intelligent Manufacturing. Guangzhou, China:Elsevier,2022:2390.
[104]	CHEN J, GAO X D, ZHONG L H. Using fuzzy grey cognitive maps to model threat assessment for UAVs[C]// Proceedings of the 14th IEEE International Conference on Control and Automation.Anchorage,AK, US:IEEE,2018:594-599.
[105]	CAVALIERE D, SENATORE S, LOIA V. Proactive UAVs for cognitive contextual awareness[J]. IEEE Systems Journal, 2019, 13(3):3568-3579. doi: 10.1109/JSYST.2018.2817191
[106]	LEUNG H, CHANDANA S, WEI S. Distributed sensing based on intelligent sensor networks[J]. IEEE Circuits and Systems Magazine, 2008, 8(2):38-52.
[107]	NIKOLAKOPOULOS K, MPELOGIANNI V, GROUMPOS P, et al.Soft computing algorithms for infrastructure monitoring. Preliminary results of PROION project[C]// Proceedings of the 9th International Conference on Remote Sensing and Geoinformation of the Environment.Ayia Napa, Cyprus:SPIE,2023:12786.
[108]	AMIRKHANI A, SHIRZADEH M, KUMBASAR T, et al. A framework for designing cognitive trajectory controllers using genetically evolved interval type-2 fuzzy cognitive maps[J]. International Journal of Intelligent Systems, 2022, 37(1):305-335.
[109]	AMIRKHANI A, SHIRZADEH M, SHOJAEEFARD M H, et al. Controlling wheeled mobile robot considering the effects of uncertainty with neuro-fuzzy cognitive map[J]. ISA Transactions, 2020, 100(1):454-468.
[110]	CAVALIERE D, LOIA V, SENATORE S. A UAV-driven surveillance system to support rescue intervention[C]// Proceedings of the 9th International Conference on Computational Logistics.Vietri sul Mare, Italy:Springer,2018:124-135.
[111]	VASCAK J. Navigation based on fuzzy cognitive maps for needs of ubiquitous robotics[C]// Proceedings of the 17th IEEE World Symposium on Applied Machine Intelligence and Informatics. Herlany, Slovakia:IEEE,2019:123-128.
[112]	VASCAK J, POMSAR L, PAPCUN P, et al. Means of IoT and fuzzy cognitive maps in reactive navigation of ubiquitous robots[J]. Electronics, 2021, 10(7):809-809.
[113]	李军, 陈士超. 无人机蜂群关键技术发展综述[J]. 兵工学报, 2023, 44(9):2533-2545. doi: 10.12382/bgxb.2023.0514
	LI J, CHEN S C. Overview of key technology and its development of drone swarm[J]. Acta Armamentarii, 2023, 44(9):2533-2545. (in Chinese) doi: 10.12382/bgxb.2023.0514
[114]	宫远强, 张业鹏, 马万鹏, 等. 无人机蜂群中的群体智能涌现机理[J]. 兵工学报, 2023, 44(9):2661-2671. doi: 10.12382/bgxb.2022.1181
	GONG Y Q, ZHANG Y P, MA W P, et al. Mechanisms of group intelligence emergence in UAV swarms[J]. Acta Armamentarii, 2023, 44(9):2661-2671. (in Chinese) doi: 10.12382/bgxb.2022.1181
[115]	CAVALIERE D, SENATORE S. Towards an agent-driven scenario awareness in remote sensing environments[C]// Proceedings of the 8th IEEE Symposium Series on Computational Intelligence.Bangalore, India:IEEE,2018:1982-1989.
[116]	MENDONCA M, CHRUN I R, NEVES J F, et al. A cooperative architecture for swarm robotic based on dynamic fuzzy cognitive maps[J]. Engineering Applications of Artificial Intelligence, 2017,59:122-132.
[117]	GANGANATH N, WALKER M, LEUNG H. Fuzzy cognitive map based situation assessment framework for navigation goal detection[C]// Proceedings of the 2013 IEEE International Conference on Systems,Man,and Cybernetics.Manchester, England:IEEE,2013:1444-1449.
[118]	LUO C, WANG X J. The synchronization of K-valued fuzzy cognitive maps[J]. Fuzzy Sets and Systems, 2024,478:108851.
[119]	MIAO C Y, MIAO Y, ANGELA G, et al. A framework for multi-agent reasoning and coordination[C]// Proceedings of 2020 IASTED International Conference on Artificial Intelligence and Soft Computing.Banff, Canada:ACTA,2020:156-162.
[120]	陈军, 梁晶, 程龙, 等. 基于FCM的多无人机协同攻击决策建模方法[J]. 航空学报, 2022, 43(7):325526. doi: 10.7527/S1000-6893.2021.25526
	CHEN J, LIANG J, CHENG L, et al. Cooperative attack decision modeling method of multiple UAVs based on FCM[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(7):325526. (in Chinese) doi: 10.7527/S1000-6893.2021.25526
[121]	陈军, 张岳, 陈晓威, 等. 基于模糊灰色认知图的复杂战场智能态势感知建模方法[J]. 兵工学报, 2022, 43(5):1093-1106. doi: 10.12382/bgxb.2021.0259
	CHEN J, ZHANG Y, CHEN X W, et al. FGCM-based modeling method of intelligent situation awareness in complex[J]. Acta Armamentarii, 2022, 43(5):1093-1106. (in Chinese)
[122]	NACHAZEL T. Fuzzy cognitive maps for decision-making in dynamic environments[J]. Genetic Programming and Evolvable Machines, 2021, 22(1):101-135.
[123]	MENDONCA M, KONDO H S, BOTONI D S L, et al. Semi-unknown environments exploration inspired by swarm robotics using fuzzy cognitive maps[C]// Proceedings of the 2019 IEEE International Conference on Fuzzy Systems.New Orleans,LA, US:IEEE,2019:1-8.
[124]	MENDONCA M, PALACIOS R H C, PAPAGEORGIOU E I, et al. Multi-robot exploration using dynamic fuzzy cognitive maps and ant colony optimization[C]//Proceedings of the 2020 IEEE International Conference on Fuzzy Systems.Electr Network,NY, US:IEEE, 2020:22116.
[125]	ZHAO Z, NIU Y F. Situation-driven fuzzy cognitive maps applied in air-to-ground target attack[C]// Proceedings of the 29th Chinese Control and Decision Conference.Chongqing, China:IEEE,2017:6384-6389.
[126]	王童豪, 彭星光, 胡浩, 等. 海上有人/无人协同系统及其关键技术综述[J]. 兵工学报, 2024, 45(10):3317-3340. doi: 10.12382/bgxb.2024.0327
	WANG T H, PENG X G, HU H, et al. Maritime manned/unmanned collaborative systems and key technologies:a survey[J]. Acta Armamentarii, 2024, 45(10):3317-3340. (in Chinese)
[127]	牛轶峰, 沈林成, 李杰, 等. 无人-有人机协同控制关键问题[J]. 中国科学:信息科学, 2019, 49(5):538-554.
	NIU Y F, SHEN L C, LI J, et al. Key scientific problems in cooperation control of unmanned-manned aircraft systems[J]. Scientia Sinica Informationis, 2019, 49(5):538-554. (in Chinese)
[128]	陈军, 张新伟, 徐嘉, 等. 有人/无人机混合编队有限干预式协同决策[J]. 航空学报, 2015, 36(11):3652-3665. doi: 10.7527/S1000-6893.2015.0085
	CHEN J, ZHANG X W, XU J, et al. Human/unmanned-aerial-vehicle team collaborative decision-making with limited intervention[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36(11):3652-3665. (in Chinese) doi: 10.7527/S1000-6893.2015.0085
[129]	CHEN J, QIU X J, RONG J, et al. Design method of organizational structure for MAVs and UAVs heterogeneous team with adjustable autonomy[J]. Journal of Systems Engineering and Electronics, 2018, 29(2):286-295. doi: 10.21629/JSEE.2018.02.09
[130]	ZHAO Z, WANG C, NIU Y F, et al. Adjustable autonomy for human-UAVs collaborative searching using fuzzy cognitive maps[C]// Proceedings of the 2nd China Symposium on Cognitive Computing and Hybrid Intelligence.Xi’an, China:IEEE,2019:230-234.
[131]	钟赟, 姚佩阳, 张杰勇, 等. 基于HFCM的有人-无人机作战系统交互式协同决策[J]. 系统工程理论与实践, 2021, 41(10):2748-2760. doi: 10.12011/SETP2020-0283
	ZHONG Y, YAO P Y, ZHANG J Y, et al. Interactive cooperative decision-making of manned-unmanned aerial vehicle combat system based on HFCM[J]. Systems Engineering-Theory & Practice, 2021, 41(10):2748-2760. (in Chinese)
[132]	CHEN J, GAO X G, YU G H. Cooperative effect analysis of manned/unmanned aerial vehicle team based on fuzzy cognition map[C]// Proceedings of the 2011 IEEE International Conference on Signal Processing,Communications and Computing. Xi’an, China:IEEE,2011:1-5.
[133]	XIONG H Y, LI S, LEI T, et al. MAV-UAV collaborative situation assessment based on dynamic intuitionistic fuzzy cognitive maps[C]// Proceedings of the 2021 International Conference on Autonomous Unmanned Systems.Changsha, China:Springer,2022:2755-2764.
[134]	BAEK S H, RYU S J, KIM J H. DMQEA-FCM:an approach for preference-based decision support[C]// Proceedings of the 2016 IEEE International Conference on Fuzzy Systems.Vancouver, Canada:IEEE,2016:1983-1990.
[135]	HAN J H, KIM J H. Human intention reading by fuzzy cognitive map:A human-robot cooperative object carrying task[C]// Proceedings of the 1st International Conference on Robot Intelligence Technology and Applications.Gwangju, Korea:Springer,2013:127-135.
[136]	学喆, 张岳, 陈军. 无人-有人机混合主动式交互决策研究[J]. 航空科学技术, 2022, 33(5):44-52.
	XUE Z, ZHANG Y, CHEN J. Research on UAV-MAV mixed-initiative interactive decision[J]. Aeronautical Science & Technology, 2022, 33(5):44-52. (in Chinese)
[137]	ZHAO Z, NIU Y F, SHEN L C. Adaptive level of autonomy for human-UAVs collaborative surveillance using situated fuzzy cognitive maps[J]. Chinese Journal of Aeronautics, 2020, 33(11):2835-2850.
[138]	SOVATZIDI G, VASILAKAKIS M D, IAKOVIDIS D K. Fuzzy cognitive maps for interpretable image-based classification[C]// Proceedings of the 2022 IEEE International Conference on Fuzzy Systems.Padua, Italy:IEEE,2022:1-6.
[139]	APOSTOLOPOULOS L D, GROUMPOS P P. Fuzzy cognitive maps:their role in explainable artificial intelligence[J]. Applied Sciences-Basel, 2023, 13(6):3412.
[140]	MAHMOUDZADEH S, YAZDANI A, KALANTARI Y, et al. Holistic review of UAV-centric situational awareness:applications,limitations,and algorithmic challenges[J]. Robotics, 2024, 13(8):117.
[141]	郝肇铁, 郭斌, 赵凯星, 等. 从规则驱动到群智涌现:多机器人空地协同研究综述[J]. 自动化学报, 2024, 50(10):1877-1905.
	HAO Z T, GUO B, ZHAO K X, et al. From rule-driven to collective intelligence emergence:a review of research on multi-robot air-ground collaboration[J]. Acta Automatica Sinica, 2024, 50(10):1877-1905. (in Chinese)
[142]	TANG J, DUAN H B, LAO S Y. Swarm intelligence algorithms for multiple unmanned aerial vehicles collaboration:a comprehensive review[J]. Artificial Intelligence Review, 2023, 56(5):4295-4327.
[143]	张栋, 王孟阳, 唐硕. 面向任务的无人机集群自主决策技术[J]. 指挥与控制学报, 2022, 8(4):365-377.
	ZHANG D, WANG M Y, TANG S. Autonomous decision-making technology for task-oriented UAV swarm[J]. Journal of Command and Control, 2022, 8(4):365-377. (in Chinese)
[144]	向锦武, 董希旺, 丁文锐, 等. 复杂环境下无人集群系统自主协同关键技术[J]. 航空学报, 2022, 43(10):527570. doi: 10.7527/S1000-6893.2022.27570
	XIANG J W, DONG X W, DING W R, et al. Key technologies for autonomous cooperation of unmanned swarm systems in complex environments[J]. Acta Aeronautica et Astronautica Sinica, 2022, 43(10):527570. (in Chinese) doi: 10.7527/S1000-6893.2022.27570
[145]	喻国明, 杨雅, 修利超, 等. 认知负荷超载场景下人机协同与信任的有效性评测模式——以飞行员应急条件下的认知场景为例[J]. 学术探索, 2023, 1(10):84-94.
	YU G M, YANG Y, XIU L C, et al. Effectiveness evaluation and model construction of pilot’s human-computer collaborative decision-making in emergency scenarios[J]. Academic Exploration, 2023, 1(10):84-94. (in Chinese)
[146]	叶坤武, 牛超, 刘泽华, 等. 智能空战中的人机关系[C]// 第十一届中国指挥控制大会论文集.北京, 中国: 中国指挥与控制学会,2023:41-46.
	YE K W, NIU C, LIU Z H, et al. Human-machine relationship in intelligent air combat[C]// Proceedings of the 11th Chinese Conference of Command and Control. Beijing,China: CICC,2023:41-46. (in Chinese)
[147]	CASTRO A, SILVA F, SANTOS V. Trends of human-robot collaboration in industry contexts:handover,learning,and metrics[J]. Sensors, 2021, 21(12):4113.
[1]	曾照洋, 彭文胜, 李云凯, 等. 智能无人机集群可靠性技术内涵、发展及挑战[J]. 兵工学报, 2025, 46(3):240322. doi: 10.12382/bgxb.2024.0322
	ZENG Z Y, PENG W S, LI Y K, et al. Technical connotation,development and challenges of intelligent drone swarm reliability[J]. Acta Armamentarii, 2025, 46(3):240322.
[2]	蒲志强, 易建强, 刘振, 等. 知识和数据协同驱动的群体智能决策方法研究综述[J]. 自动化学报, 2022, 48(3):627-643.
	PU Z Q, YI J Q, LIU Z, et al. Knowledge-based and data-driven integrating methodologies for collective intelligence decision making:a survey[J]. Acta Automatica Sinica, 2022, 48(3):627-643. (in Chinese)
[3]	朱迪, 张博闻, 程雅琪, 等. 知识赋能的新一代信息系统研究现状、发展与挑战[J]. 软件学报, 2023, 34(10):4439-4462.
	ZHU D, ZHANG B W, CHENG Y Q, et al. Survey on knowledge enabled new generation information systems[J]. Journal of Software, 2023, 34(10):4439-4462. (in Chinese)
[4]	张钹, 朱军, 苏航. 迈向第三代人工智能[J]. 中国科学:信息科学, 2020, 50(9):1281-1302.
	ZHANG B, ZHU J, SU H. Toward the third generation of artificial intelligence[J]. SCIENTIA SINICA Informationis, 2020, 50(9):1281-1302. (in Chinese)
[5]	马乐乐, 刘向杰, 高福荣. 基于知识迁移的数据驱动迭代学习模型预测控制[J]. 中国科学:信息科学, 2024, 54(7):1752-1774.
	MA L L, LIU X J, GAO F R. Data-driven iterative learning model predictive control based on knowledge transfer[J]. SCIENTIA SINICA Informationis, 2024, 54(7):1752-1774. (in Chinese)
[6]	黄旭, 柳嘉润, 张远, 等. 知识与数据混合驱动的高速飞行控制方法综述[J]. 宇航学报, 2023, 44(8):1113-1126.
	HUANG X, LIU J R, ZHANG Y, et al. Review on knowledge-based and data-driven cooperating control methods of high-speed vehicle[J]. Journal of Astronautics, 2023, 44(8):1113-1126. (in Chinese)
[7]	李洋军, 黄琦龙, 杨力, 等. 数据和知识双驱动的空中集群目标作战意图识别[J]. 兵工学报, 2025, 46(2):240113. doi: 10.12382/bgxb.2024.0113
	LI Y J, HUANG Q L, YANG L, et al. Combat intention recognition of air cluster targets driven by data and knowledge[J]. Acta Armamentarii, 2025, 46(2):240113. (in Chinese) doi: 10.12382/bgxb.2024.0113
[8]	KOSKO B. Fuzzy cognitive maps[J]. International Journal of Man-Machine Studies, 1986, 24(1):65-75.
[9]	PAPAGEORGIOU E I, SALMERON J L. A review of fuzzy cognitive maps research during the last decade[J]. IEEE Transactions on Fuzzy Systems, 2012, 21(1):66-79.
[10]	张燕丽. 基于模糊认知图的动态系统的建模与控制[D]. 大连: 大连理工大学, 2012.
	ZHANG Y L. The modeling and control of dynamic system based on fuzzy cognitive maps[D]. Dalian: Dalian University of Technology, 2012. (in Chinese)
[11]	李慧, 陈红倩, 马丽仪, 等. 模糊认知图的算法改进与应用综述[J]. 南京大学学报(自然科学版), 2016, 52(4):746-761.
	LI H, CHEN H Q, MA L Y, et al. A review of algorithm improvement and application of fuzzy cognitive map[J]. Journal of Nanjing University (Natural Science), 2016, 52(4):746-761. (in Chinese)
[12]	ROTSHTEIN A, YOSEF A, NESKORODEVA T, et al. Fuzzy cognitive map and mean square method in empirical modeling:application in economics[J]. Expert Systems with Applications, 2024,247:123176.
[13]	KARATZINIS G D, POSTOLIKAS N A, BOUTALIS Y S, et al. Fuzzy cognitive networks in diverse applications using hybrid representative structures[J]. International Journal of Fuzzy Systems, 2023, 25(7):2534-2554.
[14]	CHEN J, GAO X D, RONG J, et al. A situation awareness assessment method based on fuzzy cognitive maps[J]. Journal of Systems Engineering and Electronics, 2022, 33(5):1108-1122. (in Chinese)
[15]	APOSTOLOPOULOS I D, PAPANDRIANOS N I, PAPATHANA-SIOU N D, et al. Fuzzy cognitive map applications in medicine over the last two decades:a review study[J]. Bioengineering-Basel, 2024, 11(2):139-139.
[16]	FONSECA K, ESPITIA E, BREUER L, et al. Using fuzzy cognitive maps to promote nature-based solutions for water quality improvement in developing-country communities[J]. Journal of Cleaner Production, 2022,377:134246.
[17]	FELIX G, NAPOLES G, FALCON R, et al. A review on methods and software for fuzzy cognitive maps[J]. Artificial Intelligence Review, 2019, 52(3):1707-1737. doi: 10.1007/s10462-017-9575-1
[18]	POCZETA K, KUBUS L, YASTREBOV A. Analysis of an evolutionary algorithm for complex fuzzy cognitive map learning based on graph theory metrics and output concepts[J]. Biosystems, 2019,179:39-47.
[19]	刘晓倩, 张英俊, 秦家虎, 等. 模糊认知图学习算法及应用综述[J]. 自动化学报, 2024, 50(3):450-474.
	LIU X Q, ZHANG Y J, QIN J H, et al. A review of fuzzy cognitive map learning algorithms and applications[J]. Acta Automatica Sinica, 2024, 50(3):450-474. (in Chinese)
[20]	SCHUERKAMP R, GIABBANELLI P J. Extensions of fuzzy cognitive maps:a systematic review[J]. ACM Computing Surveys, 2023, 56(2):53.
[21]	SINGH R, NISHAD D K, KHALID S, et al. A review of the application of fuzzy mathematical algorithm-based approach in autonomous vehicles and drones[J]. International Journal of Intelligent Robotics and Applications, 2025,9:344-364.
[22]	BUENO S, SALMERON J L. Benchmarking main activation functions in fuzzy cognitive maps[J]. Expert Systems with Applications, 2009, 36(3):5221-5229.
[23]	STYLIOS C D, GROUMPOS P P. A soft computing approach for modelling the supervisor of manufacturing systems[J]. Journal of Intelligent and Robotic Systems, 1999, 26(3):389-403.
[24]	HAGIWARA M. Extended fuzzy cognitive maps[C]// Proceedings of IEEE International Conference on Fuzzy Systems.San Diego,CA, US:IEEE,1992:795-801.
[25]	GIABBANELLI P J, GALGOCZY M C, NGUYEN D M, et al. Mapping the complexity of suicide by combining participatory modeling and network science[C]// Proceedings of the 13rd IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining. Hague,the Netherlands: ACM,2021:339-342.
[26]	GUPTA V K, GIABBANELLI P J, TAWFIK A A. An online environment to compare students’ and expert solutions to ill-structured problems[C]// Proceedings of the 5th International Conference on Learning and Collaboration Technologies.Las Vegas,NV, US:Springer,2018:286-307.
[27]	SARMIENTO I, COCKCROFT A, DION A, et al. Combining conceptual frameworks on maternal health in indigenous communities-fuzzy cognitive mapping using participant and operator-independent weighting[J]. Field Methods, 2022, 34(3):223-239.
[28]	STACH W, KURGAN L, PEDRYCZ W, et al. Genetic learning of fuzzy cognitive maps[J]. Fuzzy Sets and Systems, 2005, 153(3):371-401.
[29]	HILAL A M, ALSOLAI H, ALWESABI F N, et al. Fuzzy cognitive maps with bird swarm intelligence optimization-based remote sensing image classification[J]. Computational Intelligence and Neuroscience, 2022, 2022(1):4063354.
[30]	PAPAGEORGIOU E I. A new methodology for decisions in medical informatics using fuzzy cognitive maps based on fuzzy rule-extraction techniques[J]. Applied Soft Computing, 2011, 11(1):500-513.
[31]	LIU J, CHI Y X, ZHU C. A dynamic multiagent genetic algorithm for gene regulatory network reconstruction based on fuzzy cognitive maps[J]. IEEE Transactions on Fuzzy Systems, 2015, 24(2):419-431.
[32]	Al DUHAYYIM M, MOHAMED H G, ALZAHRANI J S, et al. Modeling of fuzzy cognitive maps with a metaheuristics-based rainfall prediction system[J]. Sustainability, 2023, 15(1):25.
[33]	迟亚雄. 模糊认知图智能学习算法与应用研究[D]. 西安: 西安电子科技大学, 2017.
	CHI Y X. Research on intelligent learning algorithmand application of fuzzy cognitive maps[D]. Xi’an: Xidian University, 2017. (in Chinese)
[34]	吴凯. 复杂系统智能建模算法及其应用研究[D]. 西安: 西安电子科技大学, 2020.
	WU K. Intelligent modeling algorithm for complex system and its application[D]. Xi’an: Xidian University, 2020. (in Chinese)
[35]	DICKERSON J A, KOSKO B. Virtual worlds as fuzzy cognitive maps[J]. Presence:Teleoperators & Virtual Environments, 1994, 3(2):173-189.
[36]	HUERGA A V. A balanced differential learning algorithm in fuzzy cognitive maps[C]// Proceedings of the 16th International Workshop on Qualitative Reasoning.Sitges, Spain:Elsevier,2002:1-7.
[37]	PAPAGEORGIOU E I, STYLISO C D, GROUMPOS P P. Active Hebbian learning algorithm to train fuzzy cognitive maps[J]. International Journal of Approximate Reasoning, 2004, 37(3):219-249.
[38]	PAPAKOSTAS G A, POLYDOROS A S, KOULOURIOTIS D E, et al. Training fuzzy cognitive maps by using Hebbian learning algorithms:a comparative study[C]// Proceedings of the 2011 IEEE International Conference on Fuzzy Systems.Taipei, China:IEEE,2011:851-858.
[39]	STACH W, KURGAN L, PEDRYCZ W. Data-driven nonlinear Hebbian learning method for fuzzy cognitive maps[C]// Proceedings of the 2008 IEEE International Conference on Fuzzy Systems.Hong Kong, China:IEEE,2008:1975-1981.
[40]	KOULOURIOTIS D E, DIAKOULAKIS I E, EMRIIS D M. Learning fuzzy cognitive maps using evolution strategies:a novel schema for modeling and simulating high-level behavior[C]// Proceedings of the 2001 Congress on Evolutionary Computation.Seoul, South Korea: IEEE,2001:364-371.
[41]	PARSOPOULOS K E, PAPAGEORGIOU E I, GROUMPOS P P, et al. A first study of fuzzy cognitive maps learning using particle swarm optimization[C]// Proceedings of the 2003 Congress on Evolutionary Computation.Canberra, Australia:IEEE,2003:1440-1447.
[42]	PAPAGEORGIOU E I, GROUMPOS P P. Optimization of fuzzy cognitive map model in clinical radiotherapy through differential evolution algorithm[J]. International Journal of Biomedical Soft Computing and Human Sciences, 2003, 9(2):25-31.
[43]	STACH W, KURGAN L, PEDRYCZ W. Parallel learning of large fuzzy cognitive maps[C]// Proceedings of the 2007 IEEE International Joint Conference on Neural Networks.Orlando,FL, US:IEEE,2007:1584-1589.
[44]	YESIL E, URBAS L. Big bang-big crunch learning method for fuzzy cognitive maps[J]. International Journal of Computer and Information Engineering, 2010, 4(11):1756-1765.