New Results on Stability for a Class of Fractional-Order Static Neural Networks

被引：10

作者：

Yao, Xiangqian ^{[1
]}

Tang, Meilan ^{[1
]}

Wang, Fengxian ^{[2
]}

Ye, Zhijian ^{[1
]}

Liu, Xinge ^{[1
]}

机构：

[1] Cent South Univ, Sch Math & Stat, Changsha 410083, Peoples R China

[2] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China

来源：

CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2020年 / 39卷 / 12期

基金：

美国国家科学基金会;

关键词：

Fractional-order; Projection neural networks; Convex Lyapunov function; Mittag-Leffler stability; Linear matrix inequality; ASYMPTOTIC STABILITY; LYAPUNOV FUNCTIONS; ROBUST STABILITY; SYNCHRONIZATION; SYSTEMS;

D O I：

10.1007/s00034-020-01451-5

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper investigates the stability of a class of fractional-order static neural networks. Two new Lyapunov functions with proper integral terms are constructed. These integrals with variable upper limit are convex functions. Based on the fractional-order Lyapunov direct method and some inequality skills, several novel stability sufficient conditions which ensure the global Mittag-Leffler stability of fractional-order projection neural networks (FPNNs) are presented in the forms of linear matrix inequalities (LMIs). Two LMI-based Mittag-Leffler stability criteria with less conservativeness are given for a special kind of FPNNs. Finally, the effectiveness of the proposed method is demonstrated via four numerical examples.

引用

页码：5926 / 5950

页数：25

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