Impulsive synchronization of two coupled delayed reaction-diffusion neural networks using time-varying impulsive gains

被引：8
作者：
Chen, Wu-Hua [1 ]
Deng, Xiaoqing [2 ]
Lu, Xiaomei [2 ]
机构：
[1] Guangxi Univ, Sch Elect Engn, Nanning 530004, Guangxi, Peoples R China
[2] Guangxi Univ, Coll Math & Informat Sci, Nanning 530004, Guangxi, Peoples R China
来源：
NEUROCOMPUTING | 2020年 / 377卷
基金：
中国国家自然科学基金;
关键词：
Impulsive synchronization; Reaction-diffusion neural networks; Time-varying impulsive gains; Impulse-time-dependent discretized; Lyapunov functions; H-INFINITY SYNCHRONIZATION; EXPONENTIAL SYNCHRONIZATION; INTERMITTENT SYNCHRONIZATION; ADAPTIVE SYNCHRONIZATION; L-2-GAIN ANALYSIS; MIXED DELAYS; STABILITY; SYSTEMS; TERMS; STABILIZATION;
D O I：
10.1016/j.neucom.2019.08.098
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the problem of impulsive synchronization of two coupled delayed reaction-diffusion neural networks under aperiodic discrete measurements is revisited. Different from the previous static impulsive gain based impulsive synchronization strategy, a novel impulsive synchronization strategy using sampling-interval-dependent impulsive gains is proposed. The time-varying impulsive synchronization gains are able to adapt to the variation of sampling intervals, and thus can improve the synchronization performance. The stability analysis of the resultant synchronization error system is performed by applying an impulse-time-dependent discretized Lyapunov functions based method. Sufficient conditions for the existence of desired impulsive synchronization controllers are derived in terms of a set of linear matrix inequalities (LMIs). These conditions allow to synthesize time-varying impulsive gains. A numerical example is presented to demonstrate the effectiveness of the developed methodology. (c) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：334 / 344
页数：11
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