Lagrange Exponential Stability of Complex-Valued BAM Neural Networks With Time-Varying Delays

被引：74

作者：

Zhang, Ziye ^{[1
,2
]}

Guo, Runan ^{[2
]}

Liu, Xiaoping ^{[3
]}

Lin, Chong ^{[4
]}

机构：

[1] Shandong Univ Sci & Technol, Coll Elect Engn & Automat, Qingdao 266590, Peoples R China

[2] Shandong Univ Sci & Technol, Coll Math & Syst Sci, Qingdao 266590, Peoples R China

[3] Lakehead Univ, Dept Elect Engn, Thunder Bay, ON P7B 5E1, Canada

[4] Qingdao Univ, Inst Complex Sci, Qingdao 266071, Peoples R China

来源：

IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2020年 / 50卷 / 08期

基金：

中国国家自然科学基金; 加拿大自然科学与工程研究理事会;

关键词：

Control theory; Stability analysis; Delays; Biological neural networks; Asymptotic stability; Complex-valued bidirectional associative memory (BAM) neural networks; Lagrange exponential stability; time-varying delays; TO-STATE STABILITY; GLOBAL STABILITY; NEUTRAL-TYPE; SENSE; SYNCHRONIZATION; MULTISTABILITY; EXISTENCE; DISCRETE;

D O I：

10.1109/TSMC.2018.2840091

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper is concerned with the Lagrange exponential stability problem of complex-valued bidirectional associative memory neural networks with time-varying delays. On the basis of activation functions satisfying different assumption conditions, by combining the Lyapunov function approach with some inequalities techniques, different sufficient criteria including algebraic conditions and the condition in terms of LMI are derived to guarantee Lagrange exponential stability of the addressed system, respectively. Moreover, the estimations of different globally attractive sets named the convergence balls are also provided. In the end, the effectiveness and superiority-inferiority of these different results are verified by illustrative examples.

引用

页码：3072 / 3085

页数：14

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