Dissipativity Analysis of Switched Gene Regulatory Networks Actuated by Persistent Dwell-Time Switching Strategy

被引:8
作者
Shen, Hao [1 ,2 ]
Huang, Zhengguo [1 ]
Xia, Jianwei [3 ]
Cao, Jinde [2 ]
Park, Ju H. [4 ]
机构
[1] Anhui Univ Technol, Sch Elect & Informat Engn, Maanshan 243002, Peoples R China
[2] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[3] Liaocheng Univ, Sch Math Sci, Liaocheng 252059, Shandong, Peoples R China
[4] Yeungnam Univ, Dept Elect Engn, Gyongsan 38541, South Korea
来源
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2021年 / 51卷 / 09期
基金
中国国家自然科学基金; 新加坡国家研究基金会;
关键词
Switches; Proteins; Gene expression; Stability analysis; Biological system modeling; Control theory; Dissipativity property; global uniform exponential stability; persistent dwell-time switching strategy; switched gene regulatory networks; NONLINEAR-SYSTEMS; STATE ESTIMATION; NEURAL-NETWORKS; H-INFINITY; STOCHASTIC STABILITY; FEEDBACK-CONTROL; LINEAR-SYSTEMS; STABILIZATION; EXPRESSION;
D O I
10.1109/TSMC.2019.2956281
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
This article concentrates on the global uniform exponential stability analysis and dissipativity property for a class of discrete-time gene regulatory networks (GRNs). To describe the processes at work as cells change phenotype, the time-dependent persistent dwell-time switching strategy is applied to switched GRNs. Formulating the mode-dependent Lyapunov-Krasovskii functional, the global uniform exponential stability and strictly dissipativity property criteria for the GRNs subject to time-varying delays are presented. Ultimately, an example, including three cases is employed to illustrate and discuss the availability of the given criteria.
引用
收藏
页码:5535 / 5546
页数:12
相关论文
共 50 条
  • [1] Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay
    Ahn, Choon Ki
    [J]. INFORMATION SCIENCES, 2010, 180 (23) : 4582 - 4594
  • [2] Alon U., An introduction to systems biology: Design principles of biological circuits
  • [3] Robust state estimation for discrete-time genetic regulatory network with random delays
    Balasubramaniam, P.
    Banu, L. Jarina
    [J]. NEUROCOMPUTING, 2013, 122 : 349 - 369
  • [4] Joint state filtering and parameter estimation for linear stochastic time-delay systems
    Basin, Michael
    Shi, Peng
    Calderon-Alvarez, Dario
    [J]. SIGNAL PROCESSING, 2011, 91 (04) : 782 - 792
  • [5] Exponential stability of discrete-time genetic regulatory networks with delays
    Cao, Jinde
    Ren, Fengli
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS, 2008, 19 (03): : 520 - 523
  • [6] Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture
    Chen, C. L. Philip
    Liu, Zhulin
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (01) : 10 - 24
  • [7] A New Learning Algorithm for a Fully Connected Neuro-Fuzzy Inference System
    Chen, C. L. Philip
    Wang, Jing
    Wang, Chi-Hsu
    Chen, Long
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (10) : 1741 - 1757
  • [8] Adaptive Consensus Control for a Class of Nonlinear Multiagent Time-Delay Systems Using Neural Networks
    Chen, C. L. Philip
    Wen, Guo-Xing
    Liu, Yan-Jun
    Wang, Fei-Yue
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (06) : 1217 - 1226
  • [9] Stochastic Switching in Gene Networks Can Occur by a Single-Molecule Event or Many Molecular Steps
    Choi, Paul J.
    Xie, X. Sunney
    Shakhnovich, Eugene I.
    [J]. JOURNAL OF MOLECULAR BIOLOGY, 2010, 396 (01) : 230 - 244
  • [10] On Extended Dissipativity of Discrete-Time Neural Networks With Time Delay
    Feng, Zhiguang
    Zheng, Wei Xing
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2015, 26 (12) : 3293 - 3300