State quantized sampled-data control design for complex-valued memristive neural networks

被引：37

作者：

Cai, Li ^{[1
]}

Xiong, Lianglin ^{[1
]}

Cao, Jinde ^{[2
,3
]}

Zhang, Haiyang ^{[1
]}

Alsaadi, Fawaz E. ^{[4
]}

机构：

[1] Yunnan Minzu Univ, Sch Math & Comp Sci, Kunming 650504, Yunnan, Peoples R China

[2] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China

[3] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea

[4] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah 21589, Saudi Arabia

来源：

JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS | 2022年 / 359卷 / 09期

基金：

中国国家自然科学基金;

关键词：

STABILITY ANALYSIS; SYNCHRONIZATION CONTROL; DYNAMICAL NETWORKS; CONTINUOUS-TIME; SYSTEMS; STABILIZATION; INEQUALITY;

D O I：

10.1016/j.jfranklin.2022.04.016

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

In this paper, several resultful control schemes based on data quantization are proposed for complex-valued memristive neural networks (CVMNNs). Firstly, considering the finite communication resources and the interference of failures to the system, a state quantized sampled-data controller (SQSDC) is designed for CVMNNs. Next, taking the interference of gain fluctuations into account, a non-fragile sampled-data control (SDC) law is proposed for CVMNNs in the framework of data quantification. In order to full capture more inner sampling information, a newly Lyapunov-Krasovskii function (LKF) is constructed on the basis of the proposed triple integral inequality. After that, in the framework of taking full advantage of the property of Bessel-Legendre inequality, a time-dependent discontinuous LKF (TDDLKF) is proposed for CVMNNs with SQSDC. Based on the useful LKF, several stability criteria are established. Finally, the numerical simulations are provided to substantiate the validity and less conservatism of the proposed schemes. (C) 2022 The Franklin Institute. Published by Elsevier Ltd. All rights reserved.

引用

页码：4019 / 4053

页数：35

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