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Exponential and fixed‐time stabilization of memristive neural networks with mixed delays
Author(s) -
Liu Yun,
Gao Xingbao
Publication year - 2021
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.7260
Subject(s) - mathematics , control theory (sociology) , lyapunov function , artificial neural network , fixed point , equilibrium point , controller (irrigation) , exponential function , function (biology) , class (philosophy) , exponential stability , exponential growth , fixed point theorem , control (management) , nonlinear system , computer science , mathematical analysis , differential equation , artificial intelligence , physics , quantum mechanics , evolutionary biology , agronomy , biology
In this paper, we study a class of memristive neural networks with mixed delays. The existence of its equilibrium point is proved without the boundedness and initial value of the activation function, and a criterion to ensure its exponential stabilization under a sampled‐data controller is obtained by constructing an appropriate Lyapunov function. Meanwhile, the fixed‐time stabilization of memristive neural networks is also proved by the Lyapunov method. The obtained results extend and enhance some existing ones, and are illustrated by numerical examples.