Exponential synchronization of memristive neural networks with time-varying delays via quantized sliding-mode control

被引：31
作者：
Sun, Bo [1 ]
Wang, Shengbo [1 ]
Cao, Yuting [2 ]
Guo, Zhenyuan [2 ]
Huang, Tingwen [3 ]
Wen, Shiping [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Peoples R China
[2] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[3] Texas A&M Univ Qatar, Sci Program, Doha 23874, Qatar
来源：
NEURAL NETWORKS | 2020年 / 126卷
关键词：
Memristive neural network; Time-varying delay; Super-twisting algorithm; Quantization function; Exponential synchronization; PASSIVITY; SYSTEMS;
D O I：
10.1016/j.neunet.2020.03.014
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In the paper, exponential synchronization issue is considered for memristive neural networks (MNNs) with time-varying delays via quantized sliding-mode algorithm. Quantized Sliding-mode controller is introduced to ensure the slave system can be exponentially synchronized with the host system via the super-twisting algorithm, which has been proved in the main results. Quantization function consists of uniform quantizer and logarithmic quantizer. Simulation results are given with comparisons between two quantizers in the end. (c) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码：163 / 169
页数：7
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