Global asymptotic stability of fractional-order complex-valued neural networks with probabilistic time-varying delays

被引：31

作者：

Chen, Sihan ^{[1
]}

Song, Qiankun ^{[2
]}

Zhao, Zhenjiang ^{[3
]}

Liu, Yurong ^{[4
,5
]}

Alsaadi, Fuad E. ^{[6
]}

机构：

[1] Chongqing Jiaotong Univ, Sch Econ & Management, Chongqing 400074, Peoples R China

[2] Chongqing Jiaotong Univ, Dept Math, Chongqing 400074, Peoples R China

[3] Huzhou Univ, Dept Math, Huzhou 313000, Peoples R China

[4] Yangzhou Univ, Dept Math, Yangzhou 225002, Jiangsu, Peoples R China

[5] Yancheng Inst Technol, Sch Math & Phys, Yancheng 224051, Peoples R China

[6] King Abdulaziz Univ, Fac Engn, Commun Syst & Networks CSN Res Grp, Jeddah 21589, Saudi Arabia

来源：

NEUROCOMPUTING | 2021年 / 450卷

基金：

中国国家自然科学基金;

关键词：

Complex-valued neural networks; Fractional-order calculus; Stability; Probabilistic time-varying delays; Linear matrix inequality; EXPONENTIAL STABILITY; ROBUST STABILITY; STATE ESTIMATION; LEAKAGE;

D O I：

10.1016/j.neucom.2021.04.043

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The stability of fractional-order complex-valued neural networks (FOCVNNs) with probabilistic time varying delays is investigated in this paper. By constructing suitable Lyapunov-Krasovskii functional and utilizing inequality technique, a complex-valued linear matrix inequality (LMI) criterion guaranteeing the global asymptotic stability of the proposed FOCVNNs is deduced. A numerical example with simulations is provided to demonstrate the feasibility and availability of the obtained theoretical result. (c) 2021 Elsevier B.V. All rights reserved.

引用

页码：311 / 318

页数：8

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