Improving controllability of symmetric network based on time segmentation

被引:0
|
作者
Wang, Li-Fu [1 ]
Kou, Xiao-Yu [1 ]
Kong, Zhi [1 ]
Guo, Ge [1 ]
机构
[1] School of Control Engineering, Northeastern University at Qinhuangdao, Qinhuangdao,066004, China
来源
Kongzhi yu Juece/Control and Decision | 2024年 / 39卷 / 08期
关键词
D O I
10.13195/j.kzyjc.2023.0202
中图分类号
学科分类号
摘要
Network structure has a great impact on the realization of complete network control. Therefore, it is of great significance to optimize the controllability of complex networks only based on network structure without adding drive nodes. This paper proposes a method of dividing a static symmetric network into a dynamic time-varying network composed of multiple snapshots (each snapshot is a static network), which uses the advantages of the time-varying network to reduce the number of driving nodes and improve the network controllability. The controllability criteria of the time-varying symmetric network composed of multiple snapshots, the optimal partition of snapshots, and the relationship between the number of drive nodes and the number of snapshots are given. The application process of the partition method is illustrated by an actual example, and the simulation results are verified in the model network and the real network. The results show that the method of time segmentation can effectively reduce the number of driving nodes in the symmetric network and improve the network controllability. © 2024 Northeast University. All rights reserved.
引用
收藏
页码:2671 / 2678
相关论文
共 50 条
  • [41] A Symmetric Fully Convolutional Residual Network With DCRF for Accurate Tooth Segmentation
    Rao, Yunbo
    Wang, Yilin
    Meng, Fanman
    Pu, Jiansu
    Sun, Jihong
    Wang, Qifei
    IEEE ACCESS, 2020, 8 : 92028 - 92038
  • [42] Topology-Based Controllability Problem in Network Systems
    Haghighi, Reze
    Cheah, Chien Chern
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (11): : 3077 - 3088
  • [43] Study On Network Structural Controllability Based On Topology Decomposition
    Han, Hezhen
    Li, Xiaoli
    Zhao, Shuguang
    2015 27TH CHINESE CONTROL AND DECISION CONFERENCE (CCDC), 2015, : 244 - 249
  • [44] Knowledge-Based Prediction of Network Controllability Robustness
    Lou, Yang
    He, Yaodong
    Wang, Lin
    Tsang, Kim Fung
    Chen, Guanrong
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2022, 33 (10) : 5739 - 5750
  • [45] Segmentation of lung nodules based on a refined segmentation network
    Chen, Yang
    Hou, Xuewen
    Yang, Yifeng
    Zhou, Yichen
    Xie, Yuanzhong
    Nie, Shengdong
    MEDICAL PHYSICS, 2024, 51 (04) : 2759 - 2771
  • [46] A linear algebraic criterion for controllability of both continuous-time and discrete-time symmetric bilinear systems
    Tie, Lin
    Cai, Kai-Yuan
    Lin, Yan
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2013, 350 (04): : 898 - 910
  • [47] BiSeNet: Bilateral Segmentation Network for Real-Time Semantic Segmentation
    Yu, Changqian
    Wang, Jingbo
    Peng, Chao
    Gao, Changxin
    Yu, Gang
    Sang, Nong
    COMPUTER VISION - ECCV 2018, PT XIII, 2018, 11217 : 334 - 349
  • [48] A semi-symmetric domain adaptation network based on multi-level adversarial features for meningioma segmentation
    Wang, Zizhou
    Shu, Xin
    Chen, Chaoyue
    Teng, Yuen
    Zhang, Lei
    Xu, Jianguo
    KNOWLEDGE-BASED SYSTEMS, 2021, 228
  • [49] Improving Road Semantic Segmentation Using Generative Adversarial Network
    Abdollahi, Abolfazl
    Pradhan, Biswajeet
    Sharma, Gaurav
    Maulud, Khairul Nizam Abdul
    Alamri, Abdullah
    IEEE ACCESS, 2021, 9 : 64381 - 64392
  • [50] BRAIN TUMOR SEGMENTATION WITH SYMMETRIC TEXTURE AND SYMMETRIC INTENSITY-BASED DECISION FORESTS
    Bianchi, Anthony
    Miller, James V.
    Tan, Ek Tsoon
    Montillo, Albert
    2013 IEEE 10TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2013, : 748 - 751