An Improved Finite-Time and Fixed-Time Stable Synchronization of Coupled Discontinuous Neural Networks

被引:30
作者
Xiao, Qizhen [1 ]
Liu, Hongliang [1 ]
Wang, Yinkun [2 ]
机构
[1] Univ South China, Sch Math & Phys, Hengyang 421001, Peoples R China
[2] Natl Univ Def Technol, Dept Math, Changsha 410073, Hunan, Peoples R China
关键词
Synchronization; Asymptotic stability; Stability criteria; Complex networks; Lyapunov methods; Biological neural networks; Numerical stability; Discontinuous neural network; finite-time stability; fixed-time synchronization; high-precise settling time; COMPLEX DYNAMICAL NETWORKS; GLOBAL CONVERGENCE; SAMPLED-DATA; SYSTEMS; STABILITY; BEHAVIORS; PROTOCOLS; CONSENSUS; MODEL;
D O I
10.1109/TNNLS.2021.3116320
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This article focuses on the finite-time and fixed-time synchronization of a class of coupled discontinuous neural networks, which can be viewed as a combination of the Hindmarsh-Rose model and the Kuramoto model. To this end, under the framework of Filippov solution, a new finite-time and fixed-time stable theorem is established for nonlinear systems whose right-hand sides may be discontinuous. Moreover, the high-precise settling time is given. Furthermore, by designing a discontinuous control law and using the theory of differential inclusions, some new sufficient conditions are derived to guarantee the synchronization of the addressed coupled networks achieved within a finite-time or fixed-time. These interesting results can be seemed as the supplement and expansion of the previous references. Finally, the derived theoretical results are supported by examples with numerical simulations.
引用
收藏
页码:3516 / 3526
页数:11
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