Global Uniform Asymptotic Fixed Deviation Stability and Stability for Delayed Fractional-order Memristive Neural Networks with Generic Memductance

被引：39
作者：
Chen, Jiejie [1 ,3 ]
Chen, Boshan [2 ]
Zeng, Zhigang [3 ,4 ]
机构：
[1] Hubei Normal Univ, Coll Comp Sci & Technol, Huangshi 435002, Hubei, Peoples R China
[2] Hubei Normal Univ, Coll Math & Stat, Huangshi 435002, Hubei, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[4] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Hubei, Peoples R China
来源：
NEURAL NETWORKS | 2018年 / 98卷
关键词：
Memristive neural networks; Time-varying delays; Global uniform asymptotic stability; Fixed deviation stability; Discontinuous system; OUTPUT-FEEDBACK CONTROL; MARKOVIAN JUMP SYSTEMS; SYNCHRONIZATION; PERIODICITY;
D O I：
10.1016/j.neunet.2017.11.004
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 paper, we study global uniform asymptotic fixed deviation stability and stability for a wide class of memristive neural networks with time-varying delays. Firstly, a new mathematical expression of the generic memductance (memristance) is proposed according to the feature of the memristor and the general current-voltage characteristic and a new class of neural networks is designed. Next, a new concept of stability (fixed deviation stability) is proposed in order to describe veritably the stability characteristics of the discontinuous system and the sufficient conditions are given to guarantee the global uniform asymptotic fixed deviation stability and stability of the new system. Finally, two numerical examples are provided to show the applicability and effectiveness of our main results. (C) 2017 Elsevier Ltd. All rights reserved.
引用
收藏
页码：65 / 75
页数：11
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