Finite-time synchronization of memristor neural networks via interval matrix method

被引：32

作者：

Wei, Fei ^{[1
,2
]}

Chen, Guici ^{[1
,2
]}

Wang, Wenbo ^{[1
,2
]}

机构：

[1] Wuhan Univ Sci & Technol, Hubei Prov Key Lab Syst Sci Met Proc, Wuhan 430065, Peoples R China

[2] Wuhan Univ Sci & Technol, Coll Sci, Wuhan 430065, Peoples R China

来源：

NEURAL NETWORKS | 2020年 / 127卷

基金：

中国国家自然科学基金;

关键词：

Nonlinear feedback controllers; Memristor neural networks; Finite-time synchronization; Interval matrix method; EXPONENTIAL SYNCHRONIZATION; DISCRETE;

D O I：

10.1016/j.neunet.2020.04.003

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 finite-time synchronization problems of two types of driven-response memristor neural networks (MNNs) without time-delay and with time-varying delays are investigated via interval matrix method, respectively. Based on interval matrix transformation, the driven-response MNNs are transformed into a kind of system with interval parameters, which is different from the previous research approaches. Several sufficient conditions in terms of linear matrix inequalities (LMIs) are driven to guarantee finite-time synchronization for MNNs. Correspondingly, two types of nonlinear feedback controllers are designed. Meanwhile, the upper-bounded of the settling time functions are estimated. Finally, two numerical examples with simulations are given to illustrate the correctness of the theoretical results and the effectiveness of the proposed controllers. (C) 2020 Elsevier Ltd. All rights reserved.

引用

页码：7 / 18

页数：12

共 42 条

[1] Finite-time synchronization for memristor-based neural networks with time-varying delays [J].