Global exponential stability of delayed complex-valued neural networks with discontinuous activation functions

被引：18

作者：

Hu, Jin ^{[1
]}

Tan, Haidong ^{[1
]}

Zeng, Chunna ^{[2
]}

机构：

[1] Chongqing Jiaotong Univ, Sch Math & Stat, Chongqing, Peoples R China

[2] Chongqing Normal Univ, Coll Math Sci, Chongqing, Peoples R China

来源：

NEUROCOMPUTING | 2020年 / 416卷

基金：

中国国家自然科学基金;

关键词：

Complex-valued neural networks; Global exponential stability; Discontinuous activation functions; Filippov differential inclusion; DYNAMICAL BEHAVIOR; OPTIMIZATION; PERIODICITY;

D O I：

10.1016/j.neucom.2020.02.006

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 studies the global exponential stability of delayed complex-valued neural networks with discontinuous activation functions. By introducing the complex-valued Filippov differential inclusion, we construct the framework of studying the dynamical behaviors of complex-valued neural networks with discontinuous bivariate activation functions. By employing the Leray-Schauder alternative theorem and choosing an appropriate Lyapunov function, we prove the global exponential stability of delayed complex-valued neural networks with discontinuous activation functions. The numerical example provided shows the effectiveness of the obtained results. (C) 2020 Elsevier B.V. All rights reserved.

引用

页码：1 / 11

页数：11

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