Quantized synchronization of memristive neural networks with time-varying delays via super-twisting algorithm

被引：36
|
作者：
Sun, Bo [1 ]
Wen, Shiping [1 ]
Wang, Shengbo [2 ]
Huang, Tingwen [3 ]
Chen, Yiran [4 ]
Li, Peng [5 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Automat & Artificial Intelligence, Wuhan 430074, Peoples R China
[3] Texas A&M Univ Qatar, Sci Program, Doha 23874, Qatar
[4] Duke Univ, Dept Elect & Comp Engn, Durham, NC 27708 USA
[5] Univ Calif Santa Barbara, Dept Elect & Comp Engn, Santa Barbara, CA 93106 USA
来源：
NEUROCOMPUTING | 2020年 / 380卷
基金：
美国国家科学基金会;
关键词：
Memristive neural network; Super-twisting algorithm; Quantized control scheme; Synchronization; Time-varying delay; SLIDING MODE CONTROL; EXPONENTIAL SYNCHRONIZATION; STABILITY ANALYSIS; PASSIVITY; STABILIZATION; SYSTEMS; PASSIFICATION;
D O I：
10.1016/j.neucom.2019.11.003
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 investigate quantized synchronization control problem of memristive neural networks (MNNs) with time-varying delays via super-twisting algorithm. A feedback controller is introduced with quantized method. To enormously reduce the computational complexity of the controller under super-twisting algorithm, two quantized control schemes are proposed with uniform quantizer and logarithmic quantization. We obtain some sufficient conditions of specific control plans to guarantee that the driving MNNs can synchronize with the response MNNs. A neoteric Lyapunov functional is designed to analyze the synchronization problem. Finally, in this paper ending, some illustrative examples are given in support of our results. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码：133 / 140
页数：8
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