Finite-time stability for fractional-order complex-valued neural networks with time delay

被引：69
|
作者：
Hu, Taotao [1 ,2 ]
He, Zheng [2 ]
Zhang, Xiaojun [1 ]
Zhong, Shouming [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Sichuan, Peoples R China
[2] Univ Elect Sci & Technol China, Sch Management & Econ, Chengdu 611731, Sichuan, Peoples R China
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2020年 / 365卷
基金：
中国国家自然科学基金;
关键词：
Finite-time stability; Fractional-order; Complex-valued neural networks; Mittag-Leffler function; Time delay; SYNCHRONIZATION; SYSTEMS; CHAOS; SUBJECT; MODEL;
D O I：
10.1016/j.amc.2019.124715
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper explores the finite-time stability of fractional-order complex valued neural networks with time delay. By employing Laplace transform and the properties of Mittag-Leffler function, a lemma of exponent stability is developed to derive the finite-time stability conditions. Further, by using the proposed lemma and the techniques of inequalities, the finite-time stability of fractional-order complex-valued neural networks with time delay is analyzed with and without a controller. In addition, some sufficient conditions are proposed to analyze the finite-time stability of the fractional-order complex-valued neural networks and the setting time for stability is also estimated. Finally, two examples are used to verify the validity and feasibility of the proposed criteria. (C) 2019 Elsevier Inc. All rights reserved.
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页数：17
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