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Author:

Qiao, Yuanhua (Qiao, Yuanhua.) (Scholars:乔元华) | Yan, Hongyun (Yan, Hongyun.) | Duan, Lijuan (Duan, Lijuan.) (Scholars:段立娟) | Miao, Jun (Miao, Jun.)

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EI Scopus SCIE PubMed

Abstract:

As multi-gene networks transmit signals and products by synchronous cooperation, investigating the synchronization of gene regulatory networks may help us to explore the biological rhythm and internal mechanisms at molecular and cellular levels. We aim to induce a type of fractional-order gene regulatory networks to synchronize at finite-time point by designing feedback controls. Firstly, a unique equilibrium point of the network is proved by applying the principle of contraction mapping. Secondly, some sufficient conditions for finite-time synchronization of fractional-order gene regulatory networks with time delay are explored based on two kinds of different control techniques and fractional Lyapunov function approach, and the corresponding setting time is estimated. Finally, some numerical examples are given to demonstrate the effectiveness of the theoretical results. (c) 2020 Elsevier Ltd. All rights reserved.

Keyword:

Gene regulatory networks Finite-time synchronization Fractional-order Feedback control

Author Community:

  • [ 1 ] [Qiao, Yuanhua]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
  • [ 2 ] [Yan, Hongyun]Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China
  • [ 3 ] [Duan, Lijuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China
  • [ 4 ] [Duan, Lijuan]Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China
  • [ 5 ] [Duan, Lijuan]Natl Engn Lab Key Technol Informat Secur Level Pr, Beijing 100124, Peoples R China
  • [ 6 ] [Miao, Jun]Beijing Informat Sci & Technol Univ, Sch Comp Sci, Beijing 100101, Peoples R China

Reprint Author's Address:

  • 段立娟

    [Duan, Lijuan]Beijing Univ Technol, Fac Informat Technol, Beijing 100124, Peoples R China;;[Duan, Lijuan]Beijing Key Lab Trusted Comp, Beijing 100124, Peoples R China;;[Duan, Lijuan]Natl Engn Lab Key Technol Informat Secur Level Pr, Beijing 100124, Peoples R China

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Source :

NEURAL NETWORKS

ISSN: 0893-6080

Year: 2020

Volume: 126

Page: 1-10

7 . 8 0 0

JCR@2022

ESI Discipline: COMPUTER SCIENCE;

ESI HC Threshold:132

Cited Count:

WoS CC Cited Count: 60

SCOPUS Cited Count: 65

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 21

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