广东工业大学学报 ›› 2021, Vol. 38 ›› Issue (06): 9-19.doi: 10.12052/gdutxb.210100

• • 上一篇    下一篇

复杂网络动力学与智能控制

胡滨1, 关治洪1, 谢侃2, 陈关荣3   

  1. 1. 华中科技大学 人工智能与自动化学院，湖北 武汉 430074;
    2. 广东工业大学 自动化学院，广东 广州 510006;
    3. 香港城市大学 电机工程系，香港 999077
  • 收稿日期:2021-07-06 出版日期:2021-11-10 发布日期:2021-11-09
  • 通信作者: 陈关荣(1947–)，男，教授，博士，欧洲科学院院士，主要研究方向为复杂网络、控制系统等，E-mail：eegchen@cityu.edu.hk E-mail:eegchen@cityu.edu.hk
  • 作者简介:胡滨(1986–)，女，副教授，博士，主要研究方向为计算神经科学、混杂智能控制等
  • 基金资助:
    国家自然科学基金资助项目(61976100)；粤港澳离散制造智能化联合实验室资助项目 (2020-2022)

Dynamics and Intelligent Control of Complex Networks

Hu Bin1, Guan Zhi-hong1, Xie Kan2, Chen Guan-rong3   

  1. 1. School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan 430074, China;
    2. School of Automation, Guangdong University of Technology, Guangzhou 510006, China;
    3. Department of Electrical Engineering, City University of Hong Kong, Hong Kong 999077, China
  • Received:2021-07-06 Online:2021-11-10 Published:2021-11-09

HTML

PDF (PC)

2912

摘要： 智能如何产生, 其动力学行为如何演化、如何控制? 针对这些问题, 本文从复杂网络和动力学系统的角度简要综述智能控制的相关研究: 讨论复杂网络、动力学系统、神经科学和智能控制交叉研究的内涵和挑战问题; 概述牵制控制、混杂控制、自适应控制及复杂网络可控性等研究进展。并探讨复杂网络动力学与智能控制在脑科学与机器行为学中的相关应用及研究方向。

关键词: 复杂网络, 动力学系统, 神经科学, 智能控制

Abstract: How does intelligence emerge? What kinds of dynamical behaviors are intertwined with intelligence and how do we control them? Concerning with these two issues, relevant studies on intelligent control are briefly surveyed from an integrated viewpoint of complex network and dynamic systems. Presented first are fundamental concepts and challenging questions that arise from the interdisciplinary research areas of complex networks, dynamical systems, neuroscience, and intelligent control. An overview of research progress on intelligent control is further presented, emphasizing pinning control, hybrid control, adaptive control, and the controllability of complex networks. Moreover, potential applications of complex network dynamics and intelligent control in the fields of brain science and machine behavior are briefly discussed, with an outlook at possible research directions.

Key words: complex network, dynamical system, neuroscience, intelligent control

中图分类号: 

  • TP13
[1] KENNEDY D, NORMAN, C. So much more to know [J]. Science, 2005, 309(5731): 78-102.
[2] BULLMORE E, SPORNS O. Complex brain networks: graph theoretical analysis of structural and functional systems [J]. Nature Reviews Neuroscience, 2009, 10(3): 186-198.
[3] LECUN Y, BENGIO Y, HINTON G. Deep learning [J]. Nature, 2015, 521(7553): 436-444.
[4] MARBLESTONE A H, WAYNE G, KORDING K P. Toward an integration of deep learning and neuroscience [J]. Frontiers in Computational Neuroscience, 2016, 10(94): 1-41.
[5] BEAR M F, CONNORS B W, PARADISO M A. Neuroscience: exploring the brain[M]. 3rd ed. Phoenix: Lippincott Williams and Wilkins, 2007.
[6] MINSKY M. Steps toward artificial intelligence [J]. Proceedings of the IRE, 1961, 49(1): 8-30.
[7] POO M M, DU J L, IP N Y, et al. China brain project: basic neuroscience, brain diseases, and brain-inspired computing [J]. Neuron, 2016, 92(3): 591-596.
[8] BASSETT D S, SPORNS O. Network neuroscience [J]. Nature Neuroscience, 2017, 20(3): 353-364.
[9] YAN G, VÉRTES P E, TOWLSON E K, et al. Network control principles predict neuron function in the Caenorhabditis elegans connectome [J]. Nature, 2017, 550(7677): 519-523.
[10] TANG E, BASSETT D S. Colloquium: control of dynamics in brain networks [J]. Reviews of Modern Physics, 2018, 90(3): 031003.
[11] GERSTNER W, KISTLER W M, NAUD R, et al. Neuronal dynamics: from single neurons to networks and models of cognition[M]. Cornwall: Cambridge University Press, 2014.
[12] PILLOW J W, SHLENS J, PANINSKI L, et al. Spatio-temporal correlations and visual signalling in a complete neuronal population [J]. Nature, 2008, 454(7207): 995-999.
[13] WANG X J. Probabilistic decision making by slow reverberation in cortical circuits [J]. Neuron, 2002, 36(5): 955-968.
[14] OLFATI-SABER R, MURRAY R M. Consensus problems in networks of agents with switching topology and time-delays [J]. IEEE Transactions on Automatic Control, 2004, 49(9): 1520-1533.
[15] CHEN G, WANG X F, LI X. Fundamentals of complex networks: models, structures and dynamics[M]. Singapore: Wiley, 2014.
[16] GUAN Z H, HU B, SHEN X M. Introduction to hybrid intelligent networks[M]. Cham: Springer, 2019.
[17] ARENAS A, DIAZ-GUILERA A, KURTHS J, et al. Synchronization in complex networks [J]. Physics Reports, 2008, 469(3): 93-153.
[18] STROGATZ S H. Exploring complex networks [J]. Nature, 2001, 410(6825): 268-276.
[19] WANG X F, CHEN G. Complex networks: small-world, scale-free and beyond [J]. IEEE Circuits and Systems Magazine, 2003, 3(1): 6-20.
[20] CHEN G. Pinning control and controllability of complex dynamical networks [J]. International Journal of Automation and Computing, 2017, 14(1): 1-9.
[21] NEPUSZ T, VICSEK T. Controlling edge dynamics in complex networks [J]. Nature Physics, 2012, 8(8): 568-573.
[22] CLOPATH C, BÜSING L, VASILAKI E, et al. Connectivity reflects coding: a model of voltage-based STDP with homeostasis [J]. Nature Neuroscience, 2010, 13(3): 344-352.
[23] MAJDANDZIC A, PODOBNIK B, BULDYREV S V, et al. Spontaneous recovery in dynamical networks [J]. Nature Physics, 2014, 10(1): 34-38.
[24] COHEN R, EREZ K, HAVLINL S, et al. Resilience of the internet to random breakdowns [J]. Physical Review E, 2000, 85(21): 4626-4628.
[25] AVENA-KOENIGSBERGER A, MISIC B, SPORNS O. Communication dynamics in complex brain networks [J]. Nature Reviews Neuroscience, 2018, 19(1): 17-33.
[26] GAO J, BARZEL B, BARABÁSI A L. Universal resilience patterns in complex networks [J]. Nature, 2016, 530(7590): 307-312.
[27] ERDOS P, RÉNYI A. On random graphs [J]. Publicationes Mathematicae, 1959, 6(1): 290-297.
[28] WATTS D J, STROGATZ S H. Collective dynamics of ‘small-world’ networks [J]. Nature, 1998, 393(6684): 440-442.
[29] BARABÁSI A L, ALBERT R. Emergence of scaling in random networks [J]. Science, 1999, 286(5439): 509-512.
[30] PORTER M A. Nonlinearity + networks: a 2020 vision[M]//KEVREKI-DLS P, CUEVAS-MARAVER J, SAXENA A. Emerging frontiers in nonlinear science. Cham: Springer, 2020.
[31] LAYEK G C. An introduction to dynamical systems and chaos[M]. New Delhi: Springer, 2015.
[32] CASSANDRAS C G. The event-driven paradigm for control, communication and optimization [J]. Journal of Control and Decision, 2014, 1(1): 3-17.
[33] GOEBEL R, SANFELICE R G, TEEL A R. Hybrid dynamical systems [J]. IEEE Control Systems Magazine, 2009, 29(2): 28-93.
[34] LORENZ E N. Deterministic nonperiodic flow [J]. Journal of the Atmospheric Sciences, 1963, 20(2): 130-141.
[35] HODGKIN A L, HUXLEY A F. A quantitative description of membrane current and its application to conduction and excitation in nerve [J]. The Journal of Physiology, 1952, 117(4): 500-544.
[36] LEWIS F L, LIU K, YESILDIREK A. Neural net robot controller with guaranteed tracking performance[C]//Proceedings of 8th IEEE International Symposium on Intelligent Control. Chicago: IEEE, 1993.
[37] HU B, GUAN Z H, LEWIS F L, et al. Adaptive tracking control of cooperative robot manipulators with Markovian switched couplings [J]. IEEE Transactions on Industrial Electronics, 2021, 68(3): 2427-2436.
[38] POVEDA J I, BENOSMAN M, TEEL A R. Hybrid online learning control in networked multiagent systems: a survey [J]. International Journal of Adaptive Control and Signal Processing, 2019, 33(2): 228-261.
[39] YU W, DELELLIS P, CHEN G, et al. Distributed adaptive control of synchronization in complex networks [J]. IEEE Transactions on Automatic Control, 2012, 57(8): 2153-2158.
[40] ULLMAN S. Using neuroscience to develop artificial intelligence [J]. Science, 2019, 363(6428): 692-693.
[41] ZENG L L, SHEN H, LIU L, et al. Unsupervised classification of major depression using functional connectivity MRI [J]. Human Brain Mapping, 2014, 35(4): 1630-1641.
[42] ZHANG J, CHENG W, LIU Z, et al. Neural, electrophysiological and anatomical basis of brain-network variability and its characteristic changes in mental disorders [J]. Brain, 2016, 139(8): 2307-2321.
[43] CURTO C, MORRISON K. Relating network connectivity to dynamics: opportunities and challenges for theoretical neuroscience [J]. Current Opinion in Neurobiology, 2019, 58(1): 11-20.
[44] WHITEWAY M R, BUTTS D A. The quest for interpretable models of neural population activity [J]. Current Opinion in Neurobiology, 2019, 58(1): 86-93.
[45] HU B, GUAN Z H, CHEN G, et al. Neuroscience and network dynamics toward brain-inspired intelligence[J/OL]. IEEE Transactions on Cybernetics [2021-07-01]. https://ieeexplore.ieee.org/document/9418554. DOI: 10.1109TCYB.2021.3071110.
[46] BREAKSPEAR M. Dynamic models of large-scale brain activity [J]. Nature Neuroscience, 2017, 20(3): 340-352.
[47] DENÈVE S, ALEMI A, BOURDOUKAN R. The brain as an efficient and robust adaptive learner [J]. Neuron, 2017, 94(5): 969-977.
[48] HOU H, ZHENG Q, ZHAO Y, et al. Neural correlates of optimal multisensory decision making under time-varying reliabilities with an invariant linear probabilistic population code [J]. Neuron, 2019, 104(5): 1010-1021.
[49] WERBOS P J. Intelligence in the brain: A theory of how it works and how to build it [J]. Neural Networks, 2009, 22(3): 200-212.
[50] CSETE M E, DOYLE J C. Reverse engineering of biological complexity [J]. Science, 2002, 295(5560): 1664-1669.
[51] WANG C, HILL D J. Learning from neural control [J]. IEEE Transactions on Neural Networks, 2006, 17(1): 130-146.
[52] DORFLER F, CHERTKOV M, BULLO F. Synchronization in complex oscillator networks and smart grids [J]. Proceedings of the National Academy of Sciences, 2013, 110(6): 2005-2010.
[53] FRADY E P, SOMMER F T. Robust computation with rhythmic spike patterns [J]. Proceedings of the National Academy of Sciences, 2019, 116(36): 18050-18059.
[54] HU B, GUAN Z H, YU X, et al. Multisynchronization of interconnected memristor-based impulsive neural networks with fuzzy hybrid control [J]. IEEE Transactions on Fuzzy Systems, 2018, 26(5): 3069-3084.
[55] LU A Y, YANG G H. Observer-based control for cyber-physical systems under denial-of-service with a decentralized event-triggered scheme [J]. IEEE Transactions on Cybernetics, 2020, 50(12): 4886-4895.
[56] YU X, KAYNAK O. Sliding mode control made smarter: A computational intelligence perspective [J]. IEEE Systems, Man, and Cybernetics Magazine, 2017, 3(2): 31-34.
[57] LIU Q, LI D, GE S S, et al. Adaptive feed forward neural network control with an optimized hidden node distribution [J]. IEEE Transactions on Artificial Intelligence, 2021, 2(1): 71-82.
[58] LEWIS F L, VARBIE D. Reinforcement learning and adaptive dynamic programming for feedback control [J]. IEEE Circuits and Systems Magazine, 2009, 9(3): 32-50.
[59] WANG X F, CHEN G. Pinning control of scale-free dynamical networks [J]. Physica A, 2002, 310(3-4): 521-531.
[60] TANG Y, GAO H, KURTHS J, et al. Evolutionary pinning control and its application in UAV coordination [J]. IEEE Transactions on Industrial Informatics, 2012, 8(4): 828-838.
[61] YU C, QIN J, GAO H. Cluster synchronization in directed networks of partial-state coupled linear systems under pinning control [J]. Automatica, 2014, 50(9): 2341-2349.
[62] GUAN Z H, HILL D J, SHEN X. On hybrid impulsive and switching systems and application to nonlinear control [J]. IEEE Transactions on Automatic Control, 2005, 50(7): 1058-1062.
[63] LI H, LI C, OUYANG D, et al. Impulsive synchronization of unbounded delayed inertial neural networks with actuator saturation and sampled-data control and its application to image encryption [J]. IEEE Transactions on Neural Networks and Learning Systems, 2021, 32(4): 1460-1473.
[64] HUANG J. Output regulation of nonlinear systems with nonhyperbolic zero dynamic [J]. IEEE Transactions on Automatic Control, 1995, 40(8): 1497-1500.
[65] LIU W, HUANG J. Cooperative output regulation with application to multi-agent consensus under switching network [J]. IEEE Transactions on Neural Networks and Learning Systems, 2020, 31(5): 1724-1734.
[66] MAYNE D Q, RAWLINGS J B, RAO C V, et al. Constrained model predictive control: stability and optimality [J]. Automatica, 2000, 36(6): 789-814.
[67] FU D, ZHANG H T, DUTTA A, et al. A cooperative distributed model predictive control approach to supply chain management [J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2020, 50(12): 4894-4904.
[68] XU S, LAM J, ZOU Y. New results on delay-dependent robust control for systems with time-varying delays [J]. Automatica, 2006, 42(2): 343-348.
[69] SHI S, XU S, FENG H. Robust fixed-time consensus tracking control of high-order multiple nonholonomic systems [J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2021, 51(3): 1869-1880.
[70] LIU L, LIU Y J, TONG S. Fuzzy-based multierror constraint control for switched nonlinear systems and its applications [J]. IEEE Transactions on Fuzzy Systems, 2019, 27(8): 1519-1531.
[71] FENG G, CAO S G, REES N W, et al. Design of fuzzy control systems with guaranteed stability [J]. Fuzzy Sets and Systems, 1997, 85(1): 1-10.
[72] ANTSAKLIS P J, NERODE A. Hybrid control systems: an introductory discussion to the special issue [J]. IEEE Transactions on Automatic Control, 1998, 43(4): 457-460.
[73] HESPANHA J P, MORSE A S. Stability of switched systems with average dwell-time[C]//Proceedings of the 38th IEEE Conference on Decision and Control. Phoenix: IEEE, 1999.
[74] THEODOSIS D, DIMAROGONAS D V. Event-triggered control of nonlinear systems with updating threshold [J]. IEEE Control Systems Letters, 2019, 3(3): 655-660.
[75] LI B, WANG Z, HAN Q L. Input-to-state stabilization of delayed differential systems with exogenous disturbances: The event-triggered case [J]. IEEE Transactions on Systems, Man, and Cybernetics:Systems, 2019, 49(6): 1099-1109.
[76] ASTROM K J, WITTENMARK B. Adaptive control[M]. New York: Addison-Wesley Publishing Company, 1989.
[77] LIN C T. Structural controllability [J]. IEEE Transactions on Automatic Control, 1974, 19(3): 201-208.
[78] LIU Y Y, SLOTINE J J, BARÁBASI A L. Controllability of complex networks [J]. Nature, 2011, 473(7346): 167-173.
[79] WANG L, WANG X F, CHEN G. Controllability of networked higher-dimensional systems with one-dimensional communication channels [J]. Royal Philosophical Transactions A, 2017, 375(2088): 20160215.
[80] WANG L, CHEN G, WANG X F, et al. Controllability of networked MIMO systems [J]. Automatica, 2016, 69: 405-409.
[81] GUAN Z H, QIAN T H, YU X. Controllability and observability of linear time-varying impulsive systems [J]. IEEE Transactions on Circuits and Systems I, 2002, 49(8): 1198-1208.
[82] HOU B Y, LI X, CHEN G. Structural controllability of temporally switching networks [J]. IEEE Transactions on Circuits and Systems I, 2016, 63(10): 1771-1781.
[83] BI G Q, POO M M. Synaptic modifications in cultured hippocampal neurons: dependence on spike timing, synaptic strength, and postsynaptic cell type [J]. Journal of Neuroscience, 1998, 18(24): 10464-10472.
[84] RAHWAN I, CEBRIAN M, OBRADOVICH N, et al. Machine behaviour [J]. Nature, 2019, 568(7753): 477-486.
[85] BONNET F, MILLS R, SZOPEK M, et al. Robots mediating interactions between animals for interspecies collective behaviors [J]. Science Robotics, 2019, 4(28): 1-8.
[86] EDELMAN B J, MENG J, SUMA D, et al. Noninvasive neuroimaging enhances continuous neural tracking for robotic device control [J]. Science Robotics, 2019, 4(31): 1-13.
[87] CHEN L, HU B, GUAN Z H, et al. Multiagent meta-reinforcement learning for adaptive multi-path routing optimization[J/OL]. IEEE Transactions on Neural Networks and Learning Systems [2021-07-01]. https://ieeexplore.ieee.org/document/9410247. DOI: 10.1109/TNNLS.2021.3070584.
[88] CHEN G, LOU Y, WANG L. A comparative study on controllability robustness of complex networks [J]. IEEE Transactions on Circuits and SystemsⅡ, 2019, 66(5): 828-832.
[1] 莫赞, 范梦婷, 刘洪伟, 严杨帆. 基于在线用户行为的产品非对称竞争市场结构研究[J]. 广东工业大学学报, 2023, 40(02): 111-119.
[2] 郑子钊, 彭世国, 付志文, 徐云剑. 脉冲控制下一类多权重复杂网络的鲁棒H∞同步[J]. 广东工业大学学报, 2021, 38(03): 55-61.
[3] 陆靖桥, 傅秀芬, 蒙在桥. 考虑社群结构的复杂网络的级联故障抵制模型[J]. 广东工业大学学报, 2016, 33(05): 22-27.
[4] 后锐,罗智,伍嘉文. 深成指数时间序列的网络拓扑结构研究[J]. 广东工业大学学报, 2013, 30(3): 75-79.
[5] 黄苑虹； 梁慧冰； . 从倒立摆装置的控制策略看控制理论的发展和应用[J]. 广东工业大学学报, 2001, 18(3): 49-53.
[6] 王万良； 吴刚； . 分层多变量可拓控制器及其应用[J]. 广东工业大学学报, 1999, 16(4): 102-105.
[7] 李迪； 黄石生； 宋永伦；. 基于神经网络的焊接过程建模及控制研究[J]. 广东工业大学学报, 1995, 12(S1): 1-7.
[8] 区益善；. 分布式智能网关的研究[J]. 广东工业大学学报, 1994, 11(1): 44-44.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
版权所有 © 2017 《广东工业大学学报》编辑部
地址：广州市东风东路729号广东工业大学 邮编：510090
电话：020-37626653,020-37626139,020-37626396
邮箱：xbzrb@gdut.edu.cn
本系统由北京玛格泰克科技发展有限公司设计开发