Finite-time anti-synchronization for delayed inertial neural networks via the fractional and polynomial controllers of time variable

Ailing Li; Xinlu Ye; Ailing Li; Xinlu Ye

doi:10.3934/math.2021473

AIMS Mathematics

2021, Volume 6, Issue 8: 8173-8190. doi: 10.3934/math.2021473

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Research article

Finite-time anti-synchronization for delayed inertial neural networks via the fractional and polynomial controllers of time variable

Ailing Li ^{1
,
,},
Xinlu Ye ²

1.
College of Science, Hebei North University, Zhangjiakou, 075000, China
2.
School of Mathematics, Hunan University, Changsha, 410082, China

Received: 15 January 2021 Accepted: 25 April 2021 Published: 25 May 2021
MSC : 34K24

This paper focuses on the finite-time anti-synchronization for a class of delayed master-slave inertial neural networks. By means of using the property of quadratic inequality of one variable and designing the fractional and polynomial controllers of time variable, two sufficient conditions to assure the finite-time anti-synchronization for the master-slave delayed inertial neural networks are established. Our controllers designed related to time variable t and the study method on the finite-time anti-synchronization are different from these in the existing papers.
Citation: Ailing Li, Xinlu Ye. Finite-time anti-synchronization for delayed inertial neural networks via the fractional and polynomial controllers of time variable[J]. AIMS Mathematics, 2021, 6(8): 8173-8190. doi: 10.3934/math.2021473

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Abstract

This paper focuses on the finite-time anti-synchronization for a class of delayed master-slave inertial neural networks. By means of using the property of quadratic inequality of one variable and designing the fractional and polynomial controllers of time variable, two sufficient conditions to assure the finite-time anti-synchronization for the master-slave delayed inertial neural networks are established. Our controllers designed related to time variable t and the study method on the finite-time anti-synchronization are different from these in the existing papers.

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