Finite-Time Stability for Fractional-Order Bidirectional Associative Memory Neural Networks with Time Delays

被引：12

作者：

Xu, Chang-Jin ^{[1
]}

Li, Pei-Luan ^{[2
]}

Pang, Yi-Cheng ^{[3
]}

机构：

[1] Guizhou Univ Finance & Econ, Guizhou Key Lab Econ Syst Simulat, Guiyang 550004, Peoples R China

[2] Henan Univ Sci & Technol, Sch Math & Stat, Luoyang 471023, Peoples R China

[3] Guiyang Univ Finance & Econ, Sch Math & Stat, Guiyang 550004, Peoples R China

来源：

COMMUNICATIONS IN THEORETICAL PHYSICS | 2017年 / 67卷 / 02期

基金：

中国国家自然科学基金;

关键词：

BAM neural networks; finite-time stability; time delay; Gronwall inequality; EXPONENTIAL STABILITY; SYNCHRONIZATION; EXISTENCE; DYNAMICS;

D O I：

10.1088/0253-6102/67/2/137

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

This paper is concerned with fractional-order bidirectional associative memory (BAM) neural networks with time delays. Applying Laplace transform, the generalized Gronwall inequality and estimates of Mittag-Leffler functions, some sufficient conditions which ensure the finite-time stability of fractional-order bidirectional associative memory neural networks with time delays are obtained. Two examples with their simulations are given to illustrate the theoretical findings. Our results are new and complement previously known results.

引用

页码：137 / 142

页数：6

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