A Framework of Pinning Control for Nonperiodical Stable Behaviors of Large-Scale Asynchronous Boolean Networks

被引:9
|
作者
Zhong, Jie [1 ]
Pan, Qinyao [1 ]
Xu, Wenying [2 ]
Chen, Bo [3 ]
机构
[1] Zhejiang Normal Univ, Sch Math Sci, Jinhua 321004, Peoples R China
[2] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[3] Zhejiang Univ Technol, Dept Automat, Hangzhou 310023, Peoples R China
基金
中国国家自然科学基金;
关键词
Behavioral sciences; Stability criteria; Asymptotic stability; Computational complexity; Analytical models; Neural networks; Mathematical models; Asynchronous Boolean network; nonperiodical stable behaviors; pinning control (PC); semi-tensor product of matrices; LOGICAL MODEL; STABILITY; DIFFERENTIATION; SYNCHRONIZATION; STABILIZATION;
D O I
10.1109/TAC.2024.3351553
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this article, two pinning control (PC) schemes are proposed to achieve nonperiodical stable behaviors for asynchronous Boolean networks (BNs), from the aspects of state transition digraph (STG) and dependence digraph (DD). First, under the framework of algebraic state-space representation of asynchronous BNs, a nonuniform PC is proposed based on STG and feedback vertex set. The nonuniform pinning nodes (PNs) are determined under the transformation of certain columns of the state transition matrices. Due to the high computational complexity of using STG, a uniform PC is further proposed based on the DD of asynchronous BNs, where PNs are easily found using a feedback arc set (FAS). Compared with the nonuniform PC with computational complexity O(n2(2n)) (n is the size of network), the uniform PC has advantages of lower computational complexity O(n(2 )+n2(K)) (K is the largest indegree of in-neighbors). Finally, simulations on gene networks with different sizes are given to illustrate the effectiveness of the obtained results that only almost 1%-33% nodes are needed. Especially, as for a network with 321 genes, only two nodes (1%) are needed, which well reflects the core idea of PC approach.
引用
收藏
页码:5711 / 5726
页数:16
相关论文
共 50 条
  • [1] Stabilizing Large-Scale Probabilistic Boolean Networks by Pinning Control
    Lin, Lin
    Cao, Jinde
    Lu, Jianquan
    Zhong, Jie
    Zhu, Shiyong
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (12) : 12929 - 12941
  • [2] Pinning Synchronization of Large-Scale Boolean Networks
    Wang, Liqing
    Wu, Zheng-Guang
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2024, 69 (05) : 3404 - 3410
  • [3] Network Structure and Pinning Control for Stable Behaviors of Boolean Networks
    Yu, Zongxi
    Pan, Qinyao
    Xu, Mengxia
    Zhong, Jie
    Liu, Xiaoxu
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2022, 69 (03) : 1482 - 1486
  • [4] Distributed Pinning Set Stabilization of Large-Scale Boolean Networks
    Zhu, Shiyong
    Lu, Jianquan
    Sun, Liangjie
    Cao, Jinde
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2023, 68 (03) : 1886 - 1893
  • [5] Sensors Design for Large-Scale Boolean Networks via Pinning Observability
    Zhu, Shiyong
    Lu, Jianquan
    Zhong, Jie
    Liu, Yang
    Cao, Jinde
    IEEE TRANSACTIONS ON AUTOMATIC CONTROL, 2022, 67 (08) : 4162 - 4169
  • [6] An Improved Method for Finding Attractors of Large-Scale Asynchronous Boolean Networks
    Trinh Van Giang
    Hiraishi, Kunihiko
    2021 IEEE CONFERENCE ON COMPUTATIONAL INTELLIGENCE IN BIOINFORMATICS AND COMPUTATIONAL BIOLOGY (CIBCB), 2021, : 246 - 254
  • [7] Minimum time control of large-scale boolean control networks with constraints
    Pan, Jinfeng
    Feng, Jun-e
    Meng, Min
    Zhao, Jianli
    ASIAN JOURNAL OF CONTROL, 2019, 21 (06) : 2532 - 2542
  • [8] Control of Large-Scale Boolean Networks via Network Aggregation
    Zhao, Yin
    Ghosh, Bijoy K.
    Cheng, Daizhan
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (07) : 1527 - 1536
  • [9] Distributed Observer Design for Large-Scale Boolean Control Networks
    Zhang, Zhihua
    Leifeld, Thomas
    Zhang, Ping
    2017 AMERICAN CONTROL CONFERENCE (ACC), 2017, : 2618 - 2623
  • [10] Efficient observability verification for large-scale Boolean control networks
    Zhang, Kuize
    Johansson, Karl Henrik
    2018 37TH CHINESE CONTROL CONFERENCE (CCC), 2018, : 560 - 567