Finite-Time and Fixed-Time Synchronization of Inertial Cohen-Grossberg-Type Neural Networks with Time Varying Delays

被引：39
|
作者：
Aouiti, Chaouki [1 ]
Assali, El Abed [1 ]
El Foutayeni, Youssef [2 ]
机构：
[1] Univ Carthage, Fac Sci Bizerta, Dept Math, UR13ES47 Res Units Math & Applicat, Bizerte 7021, Tunisia
[2] Hassan II Univ Casablanca, Anal Modeling & Simulat Lab, Casablanca, Morocco
来源：
NEURAL PROCESSING LETTERS | 2019年 / 50卷 / 03期
关键词：
Inertial Cohen-Grossberg-type; Neural networks; Finite-time synchronization; Fixed-time synchronization; Time-varying delays; GLOBAL EXPONENTIAL STABILITY; MIXED DELAYS; SYSTEMS; STABILIZATION;
D O I：
10.1007/s11063-019-10018-8
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 is devoted to studying the finite-time and fixed-time of inertial Cohen-Grossberg type neural networks (ICGNNs) with time varying delays. First, by constructing a proper variable substitution, the original (ICGNNs) can be rewritten as first-order differential system. Second, by utilizing feedback controllers and constructing suitable Lyapunov functionals, several new sufficient conditions guaranteeing the finite-time and the fixed-time synchronization of ICGNNs with time varying delays are obtained based on different finite-time synchronization analysis techniques. The obtained sufficient conditions are simple and easy to verify. Numerical simulations are given to illustrate the effectiveness of the theoretical results.
引用
收藏
页码：2407 / 2436
页数：30
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