Fixed-time stabilization of fuzzy neutral-type inertial neural networks with time-varying delay

被引：41

作者：

Aouiti, Chaouki ^{[1
,2
]}

Hui, Qing ^{[3
]}

Jallouli, Hediene ^{[1
,2
]}

Moulay, Emmanuel ^{[3
,4
]}

机构：

[1] Univ Carthage, Fac Sci Bizerta, Dept Math, Res Unit Math, UR13ES47,BP W, Zarzouna 7021, Bizerta, Tunisia

[2] Univ Carthage, Fac Sci Bizerta, Dept Math, Res Unit Applicat, UR13ES47,BP W, Zarzouna 7021, Bizerta, Tunisia

[3] Univ Nebraska, Dept Elect & Comp Engn, Lincoln, NE 68588 USA

[4] Univ Poitiers, XLIM UMR CNRS 7252, 11 Bd Marie & Pierre Curie, F-86073 Poitiers 9, France

来源：

FUZZY SETS AND SYSTEMS | 2021年 / 411卷

关键词：

Inertial neural networks; Fuzzy neural networks; Time-varying delay; Fixed-time stability; FINITE-TIME; EXPONENTIAL STABILITY; SYNCHRONIZATION;

D O I：

10.1016/j.fss.2020.10.018

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

This paper addresses the problem of fixed-time stabilization for a class of fuzzy neutral-type inertial neural networks (FNTINNs) with time-varying delay. By using a novel fixed-time stability theorem for dynamical systems, two different feedback control laws are designed to ensure the fixed-time stabilization of FNTINNs with time-varying delay. The proposed theoretical results can lead to a better upper settling-time estimation compared to existing results. Finally, three simulation examples are provided to illustrate the validity of the proposed theoretical results. ? 2020 Elsevier B.V. All rights reserved. This paper addresses the problem of fixed-time stabilization for a class of fuzzy neutral-type inertial neural networks (FNTINNs) with time-varying delay. By using a novel fixed-time stability theorem for dynamical systems, two different feedback control laws are designed to ensure the fixed-time stabilization of FNTINNs with time-varying delay. The proposed theoretical results can lead to a better upper settling-time estimation compared to existing results. Finally, three simulation examples are provided to illustrate the validity of the proposed theoretical results.

引用

页码：48 / 67

页数：20

共 53 条

[1] Finite-time and fixed-time synchronization of a class of inertial neural networks with multi-proportional delays and its application to secure communication [J].