New criteria on the finite-time stability of fractional-order BAM neural networks with time delay

被引：12
|
作者：
Li, Xuemei [1 ]
Liu, Xinge [1 ]
Zhang, Shuailei [1 ]
机构：
[1] Cent South Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
来源：
NEURAL COMPUTING & APPLICATIONS | 2022年 / 34卷 / 06期
基金：
中国国家自然科学基金;
关键词：
Finite-time stability; Fractional-order; Bidirectional associative memory neural networks; Time delay; MITTAG-LEFFLER STABILITY; SYNCHRONIZATION ANALYSIS; EXPONENTIAL STABILITY; REGULATORY NETWORKS; SYSTEMS; MODEL;
D O I：
10.1007/s00521-021-06605-3
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, the finite-time stability of a class of fractional-order bidirectional associative memory neural networks(FBAMNNs) with time delay is concerned. Based on the monotonicity of function, a new inequality is proved. For 0 < alpha < 1 and 1 < alpha < 2, based on the properties of the fractional derivative, the method of step and the fractional Gronwall inequality or the generalized Gronwall inequality, some new criteria on the finite-time stability of FBAMNNs are derived. Finally, three numerical examples are provided to show the effectiveness and superiority of the criteria obtained in this paper.
引用
收藏
页码：4501 / 4517
页数：17
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