MITTAG-LEFFLER STABILITY ANALYSIS OF TEMPERED FRACTIONAL NEURAL NETWORKS WITH SHORT MEMORY AND VARIABLE-ORDER

被引：19

作者：

Gu, Chuan-Yun ^{[1
]}

Zheng, Feng-Xia ^{[1
,2
]}

Shiri, Babak ^{[3
]}

机构：

[1] Sichuan Univ Arts & Sci, Sch Math, Dazhou 635000, Peoples R China

[2] Sichuan Univ, Dept Math, Chengdu 610064, Peoples R China

[3] Neijiang Normal Univ, Coll Math & Informat Sci, Data Recovery Key Lab Sichuan Prov, Neijiang 641100, Peoples R China

来源：

FRACTALS-COMPLEX GEOMETRY PATTERNS AND SCALING IN NATURE AND SOCIETY | 2021年 / 29卷 / 08期

关键词：

Mittag-Leffler Stability; Tempered Fractional Neural Networks; Short Memory; Variable-Order Tempered Fractional Neural Networks; DIFFERENTIAL-EQUATIONS; ALGEBRAIC EQUATIONS; NUMERICAL-METHOD; ALGORITHM; SYSTEM;

D O I：

10.1142/S0218348X21400296

中图分类号：

O1 [数学];

学科分类号：

0701 ; 070101 ;

摘要：

A class of tempered fractional neural networks is proposed in this paper. Stability conditions for tempered fractional neural networks are provided by using Banach fixed point theorem. Attractivity and Mittag-Leffler stability are given. In order to show the efficiency and convenience of the method used, tempered fractional neural networks with and without delay are discussed, respectively. Furthermore, short memory and variable-order tempered fractional neural networks are proposed under the global conditions. Finally, two numerical examples are used to demonstrate the theoretical results.

引用

页数：12

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