动态饱和输入下的复杂动态网络拓扑辨识

doi:10.16182/j.issn1004731x.joss.25-0571

系统仿真学报 ›› 2026, Vol. 38 ›› Issue (5): 1174-1186.doi: 10.16182/j.issn1004731x.joss.25-0571

• • 上一篇

动态饱和输入下的复杂动态网络拓扑辨识

王浩宇¹^,², 吴亚勇¹^,², 蒋国平¹^,², 郑颖¹^,², 周宣欣¹^,²

^1.南京邮电大学自动化学院，江苏南京 210023
^2.江苏省物联网智能机器人工程研究中心，江苏南京 210023

收稿日期:2025-06-18 修回日期:2025-09-13 出版日期:2026-05-21 发布日期:2026-05-29
通讯作者: 吴亚勇
第一作者简介:王浩宇(2001-)，男，硕士生，研究方向为复杂动态网络的拓扑辨识。
基金资助:
国家自然科学基金面上项目(62373197);南京邮电大学引进人才科研启动基金(自然科学)(XK0050924063)

Topology Identification of Complex Dynamical Networks Under Dynamical Saturation Inputs

Wang Haoyu¹^,², Wu Yayong¹^,², Jiang Guoping¹^,², Zheng Ying¹^,², Zhou Xuanxin¹^,²

^1.College of Automation, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
^2.Jiangsu Engineering Laboratory for Internet of Things and Intelligent Robotics, Nanjing 210023, China

Received:2025-06-18 Revised:2025-09-13 Online:2026-05-21 Published:2026-05-29
Contact: Wu Yayong

摘要/Abstract

摘要：

针对实际工程系统中控制器输入易受动态饱和与延时动态饱和约束，导致复杂动态网络拓扑辨识难以适配真实物理场景的问题，提出一种基于驱动-响应机制的拓扑辨识方法。构建与原网络动力学特性及节点规模相同的响应网络，建立驱动-响应网络间的同步误差动力学方程；设计含动态饱和与延时动态饱和的控制器及拓扑辨识器，利用Lyapunov稳定性方法推导误差系统稳定的充分条件，实现驱动-响应网络外同步，进而精确辨识原网络拓扑结构。数值仿真结果验证了所提方法的有效性。

关键词: 复杂动态网络, 拓扑辨识, 驱动响应网络, 动态饱和, 延时动态饱和

Abstract:

In view of the problem that the controller inputs in actual engineering systems are vulnerable to the constraints of dynamical saturation and delayed dynamical saturation, which makes it difficult for the topology identification of complex dynamical networks to adapt to real physical scenarios, a topology identification method based on the drive-response mechanism was proposed. A response network with the same dynamical characteristics and node scale as the original network was constructed, and the dynamical equation of synchronization error between the drive-response networks was established. A controller with dynamical saturation and delayed dynamical saturation and a topology identifier were designed, and sufficient conditions for the stability of the error system were derived using the Lyapunov stability method to achieve the outer synchronization of the drive-response networks, thereby accurately identifying the topology structure of the original network. Numerical simulation results verify the effectiveness of the proposed method.

Key words: complex dynamical network, topology identification, drive-response network, dynamical saturation, delayed dynamical saturation

中图分类号:

TP273

王浩宇,吴亚勇,蒋国平等 . 动态饱和输入下的复杂动态网络拓扑辨识[J]. 系统仿真学报, 2026, 38(5): 1174-1186.

Wang Haoyu,Wu Yayong,Jiang Guoping,et al . Topology Identification of Complex Dynamical Networks Under Dynamical Saturation Inputs[J]. Journal of System Simulation, 2026, 38(5): 1174-1186.

图/表 7

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参考文献 34

[1]	Strogatz S H. Exploring Complex Networks[J]. Nature, 2001, 410(6825): 268-276.
[2]	Klemm Konstantin, Eguíluz Víctor M, Toral Raúl, et al. Nonequilibrium Transitions in Complex Networks: A Model of Social Interaction[J]. Physical Review E, 2003, 67(2): 026120.
[3]	罗桂兰, 郝鸿俊, 王晓, 等. 昆虫种群生态网络普适特征研究[J]. 系统仿真学报, 2021, 33(7): 1542-1553.
	Luo Guilan, Hao Hongjun, Wang Xiao, et al. Study on Universal Characteristics of Insect Population Ecological Network[J]. Journal of System Simulation, 2021, 33(7): 1542-1553.
[4]	Arenas A, Fernández A, Gómez S. Analysis of the Structure of Complex Networks at Different Resolution Levels[J]. New Journal of Physics, 2008, 10(5): 053039.
[5]	Berec Vesna. Complexity and Dynamics of Topological and Community Structure in Complex Networks[J]. The European Physical Journal Special Topics, 2017, 226(10): 2205-2218.
[6]	Lin Jingyi, Ban Yifang. Complex Network Topology of Transportation Systems[J]. Transport Reviews, 2013, 33(6): 658-685.
[7]	刘擘龙, 李喆. 基于拓扑数据分析的高维数据隐藏模式挖掘[J]. 系统仿真学报, 2019, 31(9): 1755-1762.
	Liu Bolong, Li Zhe. High-dimensional Data Hiding Pattern Mining Based on Topology Data Analysis[J]. Journal of System Simulation, 2019, 31(9): 1755-1762.
[8]	Arenas Alex, Díaz-Guilera Albert, Kurths Jurgen, et al. Synchronization in Complex Networks[J]. Physics Reports, 2008, 469(3): 93-153.
[9]	Tang Yang, Qian Feng, Gao Huijun, et al. Synchronization in Complex Networks and Its Application-A Survey of Recent Advances and Challenges[J]. Annual Reviews in Control, 2014, 38(2): 184-198.
[10]	Lu Renquan, Yu Wenwu, Jinhu Lü, et al. Synchronization on Complex Networks of Networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(11): 2110-2118.
[11]	Liu Hui, Li Yan, Li Zengyang, et al. Topology Identification of Multilink Complex Dynamical Networks via Adaptive Observers Incorporating Chaotic Exosignals[J]. IEEE Transactions on Cybernetics, 2022, 52(7): 6255-6268.
[12]	Mei Guofeng, Wu Xiaoqun, Wang Yingfei, et al. Compressive-sensing-based Structure Identification for Multilayer Networks[J]. IEEE Transactions on Cybernetics, 2018, 48(2): 754-764.
[13]	Mitra Suman, Biswas Surama, Acharyya Sriyankar. Application of Meta-heuristics on Reconstructing Gene Regulatory Network: A Bayesian Model Approach[J]. IETE Journal of Research, 2023, 69(7): 4184-4195.
[14]	Zhou D, Xiao Yangang, Zhang Yaoyu, et al. Causal and Structural Connectivity of Pulse-coupled Nonlinear Networks[J]. Physical Review Letters, 2013, 111(5): 054102.
[15]	Wang Aijuan, Dong Tao, Liao Xiaofeng. Event-triggered Synchronization Strategy for Complex Dynamical Networks with the Markovian Switching Topologies[J]. Neural Networks, 2016, 74: 52-57.
[16]	Wu Yongbao, Shen Bing, Choon Ki Ahn, et al. Intermittent Dynamic Event-triggered Control for Synchronization of Stochastic Complex Networks[J]. IEEE Transactions on Circuits and Systems I: Regular Papers, 2021, 68(6): 2639-2650.
[17]	Yang Xuetao, Wu Wei, Zhu Quanxin. Synchronization of Stochastic Complex Networks by Dynamic Periodic Adaptive Event-triggered Control[J]. IEEE Transactions on Automation Science and Engineering, 2025, 22: 18786-18795.
[18]	Bai Jing, Wu Huaiqin, Cao Jinde. Topology Identification for Fractional Complex Networks with Synchronization in Finite Time Based on Adaptive Observers and Event-triggered Control[J]. Neurocomputing, 2022, 505: 166-177.
[19]	Wang Lingyan, Wu Huaiqin, Cao Jinde. An Observer-based Topology Identification and Synchronization in Finite Time for Fractional Singularly Perturbed Complex Networks via Dynamic Event-triggered Control[J]. Neural Processing Letters, 2024, 56(3): 187.
[20]	Li Kezan, Yang Dan, Shi Changyao, et al. Identifying the Switching Topology of Dynamical Networks Based on Adaptive Synchronization[J]. Chaos, 2023, 33(12): 123109.
[21]	Wu Xiaoqun. Synchronization-based Topology Identification of Weighted General Complex Dynamical Networks with Time-varying Coupling Delay[J]. Physica A: Statistical Mechanics and Its Applications, 2008, 387(4): 997-1008.
[22]	Zhu Shuaibing, Zhou Jin, Chen Guanrong, et al. A New Method for Topology Identification of Complex Dynamical Networks[J]. IEEE Transactions on Cybernetics, 2021, 51(4): 2224-2231.
[23]	Wang Xinwei, Jiang Guoping, Wu Xu. A Novel Method of Topology Identification for General Complex Dynamical Networks with Incomplete Measurements[J]. International Journal of Modern Physics C, 2018, 29(5): 1840001.
[24]	Chen Yu, Liu Zhiwei, Qin Yuzhen. Finite-time Topology Identification of Delayed Complex Dynamical Networks and its Application[J]. Cyborg and Bionic Systems, 2024, 5: 0092.
[25]	Liu Hongfang, Tu Lilan, Yu Le. Synchronization-based Topology Identification of Uncertain Stochastic Delay Complex Networks[J]. Wuhan University Journal of Natural Sciences, 2013, 18(4): 337-342.
[26]	Xu Yuhua, Zhou Wuneng, Zhang Jincheng, et al. Topology Identification of Complex Delayed Dynamical Networks with Multiple Response Systems[J]. Nonlinear Dynamics, 2017, 88(4): 2969-2981.
[27]	Stein G. Respect the Unstable[J]. IEEE Control Systems Magazine, 2003, 23(4): 12-25.
[28]	Mahjoub A, Giri F, Derbel N. Delayed System Control in Presence of Actuator Saturation[J]. Alexandria Engineering Journal, 2014, 53(3): 553-561.
[29]	Chen Liang, Lu Junan, Tse C K. Synchronization: An Obstacle to Identification of Network Topology[J]. IEEE Transactions on Circuits and Systems II: Express Briefs, 2009, 56(4): 310-314.
[30]	Zhou Jin, Lu Junan. Topology Identification of Weighted Complex Dynamical Networks[J]. Physica A: Statistical Mechanics and its Applications, 2007, 386(1): 481-491.
[31]	Lu Jianquan, Cao Jinde. Synchronization-based Approach for Parameters Identification in Delayed Chaotic Neural Networks[J]. Physica A: Statistical Mechanics and Its Applications, 2007, 382(2): 672-682.
[32]	Lee T C, Jiang Zhongping. A Generalization of Krasovskii-lasalle Theorem for Nonlinear Time-varying Systems: Converse Results and Applications[J]. IEEE Transactions on Automatic Control, 2005, 50(8): 1147-1163.
[33]	Jinhu Lü, Chen Guanrong, Cheng Daizhan. A New Chaotic System and Beyond: The Generalized Lorenz-like System[J]. International Journal of Bifurcation and Chaos, 2004, 14(5): 1507-1537.
[34]	Li Damei, Lu Junan, Wu Xiaoqun, et al. Estimating the Bounds for the Lorenz Family of Chaotic Systems[J]. Chaos Solitons & Fractals, 2005, 23(2): 529-534.

动态饱和输入下的复杂动态网络拓扑辨识

Topology Identification of Complex Dynamical Networks Under Dynamical Saturation Inputs

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