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

被引：17
|
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.
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页码：1 / 11
页数：11
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