Globally β-Mittag-Leffler stability and β-Mittag-Leffler convergence in Lagrange sense for impulsive fractional-order complex-valued neural networks

被引：9
作者：
Li, Hui [1 ]
Kao, Yonggui [1 ]
Li, Hong-Li [2 ]
机构：
[1] Harbin Inst Technol, Dept Math, Weihai 264209, Peoples R China
[2] Xinjiang Univ, Coll Math & Syst Sci, Urumqi 830046, Peoples R China
来源：
CHAOS SOLITONS & FRACTALS | 2021年 / 148卷
基金：
中国国家自然科学基金;
关键词：
Lagrange beta-Mittag-Leffler stability; Caputo derivative; Impulsive effects; Complex-valued neural network; beta-Mittag-Leffler convergence; TIME-VARYING DELAYS; SYNCHRONIZATION PROBLEM; DIFFERENTIAL-EQUATIONS; EXPONENTIAL STABILITY; LYAPUNOV FUNCTIONS; UNIFORM STABILITY; COUPLED SYSTEM; DISCRETE;
D O I：
10.1016/j.chaos.2021.111061
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
This paper explores the globally beta-Mittag-Leffler stability in Lagrange sense for the fractional-order complex-valued neural network (FOCVNN) with impulsive effects. By Lyapunov method and matrix inequalities, some novel sufficient conditions are obtained to guarantee the globally beta-Mittag-Leffler stability in Lagrange sense for two class of complex-valued (CV) activation functions. The convergent rate is also given, which is controlled by the parameters of the addressed system. The existence and uniqueness of the solution for this system do not require consideration. To show the validity and usefulness of the results, two numerical stimulations are provided. (C) 2021 Elsevier Ltd. All rights reserved.
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页数：11
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