New finite-time synchronization of memristive Cohen-Grossberg neural network with reaction-diffusion term based on time-varying delay

被引：8
作者：
Ren, Fangmin [1 ]
Jiang, Minghui [2 ]
Xu, Hao [3 ]
Fang, Xue [1 ]
机构：
[1] China Three Gorges Univ, Coll Sci, Yichang 443002, Peoples R China
[2] China Three Gorges Univ, Coll Sci, Yichang 443000, Hubei, Peoples R China
[3] China Three Gorges Univ, Coll Elect Engn & New Energy, Yichang 443002, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2021年 / 33卷 / 09期
基金：
中国国家自然科学基金;
关键词：
Finite-time synchronization; Memristor; Reaction-diffusion; Cohen-Grossberg neural networks; EXPONENTIAL SYNCHRONIZATION; STABILITY; FEEDBACK;
D O I：
10.1007/s00521-020-05259-x
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper focuses on the finite-time synchronization of memristive Cohen-Grossberg neural networks with time delays based on the reaction-diffusion term. Two new finite-time synchronous lemmas, Lemmas 2.3 and 2.4, have been obtained through some integration techniques. Since the proposal of Lemma 2.5 solves the X-phi (u(t)) problem in the denominator, and by designing two different controllers and inequality techniques, two finite-time synchronization theorems are finally obtained. Simulations are performed according to two examples to verify the validity of the results in this paper.
引用
收藏
页码：4315 / 4328
页数：14
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