Stabilization of Second-Order Memristive Neural Networks With Mixed Time Delays via Nonreduced Order

被引：66
|
作者：
Zhang, Guodong [1 ]
Zeng, Zhigang [2 ]
机构：
[1] South Cent Univ Nationalities, Sch Math & Stat, Wuhan 430074, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Automat, Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2020年 / 31卷 / 02期
基金：
美国国家科学基金会;
关键词：
Delays; Artificial neural networks; Delay effects; Stability criteria; Asymptotic stability; Synchronization; Adaptive control; memristive neural networks (MNNs); mixed time delays; stabilization; VARYING DELAYS; EXPONENTIAL STABILIZATION; SYNCHRONIZATION ANALYSIS; STABILITY; DYNAMICS;
D O I：
10.1109/TNNLS.2019.2910125
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this brief, we investigate a class of second-order memristive neural networks (SMNNs) with mixed time-varying delays. Based on nonsmooth analysis, the Lyapunov stability theory, and adaptive control theory, several new results ensuring global stabilization of the SMNNs are obtained. In addition, compared with the reduced-order method used in the existing research studies, we consider the global stabilization directly from the SMNNs themselves without the reduced-order method. Finally, we give some numerical simulations to show the effectiveness of the results.
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页码：700 / 706
页数：7
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