Stability and synchronization of memristor-based fractional-order delayed neural networks

被引：161

作者：

Chen, Liping ^{[1
]}

Wu, Ranchao ^{[2
]}

Cao, Jinde ^{[3
,4
]}

Liu, Jia-Bao ^{[2
]}

机构：

[1] Hefei Univ Technol, Sch Elect Engn & Automat, Hefei 230009, Peoples R China

[2] Anhui Univ, Sch Math, Hefei 230039, Peoples R China

[3] Southeast Univ, Dept Math, Nanjing 210096, Jiangsu, Peoples R China

[4] King Abdulaziz Univ, Fac Sci, Dept Math, Jeddah 21589, Saudi Arabia

来源：

NEURAL NETWORKS | 2015年 / 71卷

关键词：

Fractional-order; Memristor-based neural networks; Stability; Synchronization; TIME-VARYING DELAYS; GLOBAL EXPONENTIAL SYNCHRONIZATION; MITTAG-LEFFLER STABILITY; PROJECTIVE SYNCHRONIZATION; DYNAMICS; CRITERIA; NEURONS; BRAIN; CHAOS;

D O I：

10.1016/j.neunet.2015.07.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Global asymptotic stability and synchronization of a class of fractional-order memristor-based delayed neural networks are investigated. For such problems in integer-order systems, Lyapunov-Krasovskii functional is usually constructed, whereas similar method has not been well developed for fractional-order nonlinear delayed systems. By employing a comparison theorem for a class of fractional-order linear systems with time delay, sufficient condition for global asymptotic stability of fractional memristor-based delayed neural networks is derived. Then, based on linear error feedback control, the synchronization criterion for such neural networks is also presented. Numerical simulations are given to demonstrate the effectiveness of the theoretical results. (C) 2015 Elsevier Ltd. All rights reserved.

引用

页码：37 / 44

页数：8

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