Finite-Time Synchronization of Complex-Valued Memristive-Based Neural Networks Via Hybrid Control

被引：37
|
作者：
Yu, Tianhu [1 ,2 ]
Cao, Jinde [2 ,3 ]
Rutkowski, Leszek [4 ,5 ]
Luo, Yi-Ping [6 ]
机构：
[1] Luoyang Normal Univ, Dept Math, Luoyang 471934, Peoples R China
[2] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[3] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea
[4] Czestochowa Tech Univ, Inst Computat Intelligence, PL-42200 Czestochowa, Poland
[5] Univ Social Sci, Informat Technol Inst, PL-90113 Lodz, Poland
[6] Hunan Inst Engn, Coll Elect & Informat Engn, Xiangtan 411104, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2022年 / 33卷 / 08期
基金：
中国国家自然科学基金; 中国博士后科学基金;
关键词：
Synchronization; Biological neural networks; Stability criteria; Multi-layer neural network; Perturbation methods; Neurons; Memristors; Complex-valued memristive neural networks (CVMNNs); differential inequality; finite-time stability (FTS); finite-time synchronization; impulsive control; TO-STATE STABILITY; GLOBAL STABILITY; DISSIPATIVITY; DELAY;
D O I：
10.1109/TNNLS.2021.3054967
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The finite-time synchronization problem is investigated for the master-slave complex-valued memristive neural networks in this article. A novel Lyapunov-function based finite-time stability criterion with impulsive effects is proposed and utilized to design the decentralized finite-time synchronization controller. Not only the settling time but also the attractive domain with respect to the impulsive gain and average impulsive interval, as well as initial values is derived according to the sufficient synchronization condition. Two examples are outlined to illustrate the validity of our hybrid control strategy.
引用
收藏
页码：3938 / 3947
页数：10
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