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复杂系统与复杂性科学  2025, Vol. 22 Issue (2): 90-96    DOI: 10.13306/j.1672-3813.2025.02.011
  复杂网络 本期目录 | 过刊浏览 | 高级检索 |
两类耦合超图网络状态估计研究
周宣欣, 吴亚勇, 蒋国平
1.南京邮电大学自动化学院、人工智能学院;
2.江苏省物联网智能机器人工程中心,江苏 南京 210023
On State Estimation for Hypergraphs with Two Types of Coupling
ZHOU Xuanxin, WU Yayong, JIANG Guoping
1. College of Automation and College of Artificial Intelligence, Nanjing University of Posts and Telecommunications;
2. Jiangsu Engineering Center for IOT Intelligent Robots (IOTRobot), Nanjing 210023, China
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摘要 为研究超图网络的节点状态估计问题,建立具有成对和三体相互作用的超图网络模型。针对是否考虑扩散耦合形式的两种超图网络模型,建立相应的观测器网络,并构造误差动态网络。然后,利用李雅普诺夫稳定性理论证明两种误差动态网络的渐近稳定性,推导实现状态估计所需满足的充分条件。最后,通过数值模拟验证了面对两种超图网络模型状态估计方案的准确性和有效性。结果表明,设计的方法能够准确估计是否考虑扩散耦合形式的两种超图网络节点状态,有利于提高对高阶复杂网络的估计和控制能力。
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周宣欣
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蒋国平
关键词 超图状态估计李雅普诺夫稳定性误差动态网络线性矩阵不等式    
Abstract:This paper investigates the node state estimation of hypergraphs. First, the network model of hypergraphs with pairwise and triplet interactions is built. Second, considering the presence and absence of diffusive coupling, the observer networks are established, and the error dynamical networks are constructed for the two types of hypergraph network models, respectively. Then, using the Lyapunov stability theory, the asymptotic stability of the two types of error dynamical networks is proved and sufficient conditions for state estimation are derived. Finally, the accuracy and effectiveness of the proposed method are verified by numerical simulations. The results indicate the applicability of our method in accurately estimating states within the diffusively coupled and non-diffusively coupled hypergraphs, thereby advancing our capabilities in estimating and controlling higher-order complex networks.
Key wordshypergraphs    state estimation    Lyapunov stability theory    error dynamical network    linear matrix inequalities
收稿日期: 2024-09-14      出版日期: 2025-06-03
ZTFLH:  TP273  
  O231.5  
基金资助:国家自然科学基金(62373197)
通讯作者: 吴亚勇(1992),男,江苏泰兴人,博士,讲师,主要研究方向包括复杂动态网络的同步、状态估计和拓扑识别。   
作者简介: 周宣欣(2000),男,江苏宝应人,硕士研究生,主要研究方向为复杂网络状态估计及同步能力。
引用本文:   
周宣欣, 吴亚勇, 蒋国平. 两类耦合超图网络状态估计研究[J]. 复杂系统与复杂性科学, 2025, 22(2): 90-96.
ZHOU Xuanxin, WU Yayong, JIANG Guoping. On State Estimation for Hypergraphs with Two Types of Coupling[J]. Complex Systems and Complexity Science, 2025, 22(2): 90-96.
链接本文:  
https://fzkx.qdu.edu.cn/CN/10.13306/j.1672-3813.2025.02.011      或      https://fzkx.qdu.edu.cn/CN/Y2025/V22/I2/90
[1] HU Z, DENG F, SU Y, et al. Security control of networked systems with deception attacks and packet dropouts: a discrete-time approach[J]. Journal of the Franklin Institute, 2021, 358(16): 81938206.
[2] FAN Y, DING J, LIU H, et al. Large-scale multimodal transportation network models and algorithms-Part I: the combined mode split and traffic assignment problem[J]. Transportation Research Part E: Logistics and Transportation Review, 2022, 164: 102832.
[3] OLFF H, ALONSO D, BERG M P, et al. Parallel ecological networks in ecosystems[J]. Philosophical Transactions of the Royal Society B: Biological Sciences, 2009, 364(1524): 17551779.
[4] SHI Y, WANG Y, TUO J. Distributed secure state estimation of multi-agent systems under homologous sensor attacks[J]. IEEE/CAA Journal of Automatica Sinica, 2022, 10(1): 6777.
[5] SOLTAN S, MAZAURIC D, Zussman G. Analysis of failures in power grids[J]. IEEE Transactions on Control of Network Systems, 2015, 4(2): 288300.
[6] FARZA M, M′SAAD M, ROSSIGNOL L. Observer design for a class of MIMO nonlinear systems[J]. Automatica, 2004, 40(1): 135143.
[7] TARGUI B, FRIKEL M, M′SAAD M, et al. Observer design for the state estimation of a class of communication networks[C]//18th Mediterranean Conference on Control and Automation, MED′10. Marrakech, Morocco: IEEE, 2010: 495499.
[8] FAN C X, JIANG G P. State estimation of complex dynamical network under noisy transmission channel[C]//2012 IEEE International Symposium on Circuits and Systems(ISCAS). Seoul, Korea(South): IEEE, 2012: 21072110.
[9] ZHU H, LEUNG H. A maximum likelihood approach to state estimation of complex dynamical networks with unknown noisy transmission channel[C]//2013 IEEE International Symposium on Circuits and Systems(ISCAS). Beijing, China: IEEE, 2013: 25212524.
[10] PATANIA A, PETRI G, VACCARINO F. The shape of collaborations[J]. EPJ Data Science, 2017, 6: 116.
[11] FAN Z, WU X. Identifying partial topology of simplicial complexes[J]. Chaos: an Interdisciplinary Journal of Nonlinear Science, 2022, 32(11): 113128.
[12] LI K, SHI C. Synchronization-based topology identification of multilink hypergraphs: a verifiable linear independence[J]. Nonlinear Dynamics, 2024, 112(4): 27812794.
[13] LI K, LIN Y, Wang J. Synchronization of multi-directed hypergraphs via adaptive pinning control[J]. Chaos, Solitons & Fractals, 2024, 184: 115000.
[14] SHAFI S Y, ARCAK M. Adaptive synchronization of diffusively coupled systems[J]. IEEE Transactions on Control of Network Systems, 2014, 2(2): 131141.
[15] NDOW F K, AMINZARE Z. Global synchronization analysis of non-diffusively coupled networks through Contraction Theory[DB/OL].[20240624]. http://doi.org/10.48550/arXiv.2307.00030.
[16] ZHANG Y, LATORA V, MOTTER A E. Unified treatment of synchronization patterns in generalized networks with higher-order, multilayer, and temporal interactions[J]. Communications Physics, 2021, 4(1): 195.
[17] WU C W, CHUA L O. Synchronization in an array of linearly coupled dynamical systems[J]. IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1995, 42(8): 430447.
[18] GAMBUZZA L V, DI PATTI F, GALLO L, et al. Stability of synchronization in simplicial complexes[J]. Nature communications, 2021, 12(1): 1255.
[19] LI J N, BAO W D, LI S B, et al. Exponential synchronization of discrete-time mixed delay neural networks with actuator constraints and stochastic missing data[J]. Neurocomputing, 2016, 207: 700707.
[20] BOYD S, EL GHAOUI L, FERON E, et al. Linear matrix inequalities in system and control theory[M]. Society for Industrial and Applied Mathematics, 1994.
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