Exponential Stabilization of Fuzzy Memristive Neural Networks With Multiple Time Delays Via Intermittent Control

被引：43
|
作者：
Sheng, Yin [1 ,2 ]
Huang, Tingwen [3 ]
Zeng, Zhigang [1 ,2 ]
机构：
[1] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
[2] Educ Minist China, Key Lab Image Proc & Intelligent Control, Wuhan 430074, Peoples R China
[3] Texas A&M Univ, Dept Sci Program, College Stn, TX 77843 USA
来源：
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS | 2022年 / 52卷 / 05期
基金：
中国国家自然科学基金;
关键词：
Memristors; Delay effects; Germanium; Delays; Fuzzy logic; Neural networks; Control systems; Exponential stabilization; fuzzy logic; intermittent control; memristive neural networks (MNNs); time delays; STABILITY; SYSTEMS; SYNCHRONIZATION;
D O I：
10.1109/TSMC.2021.3062381
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
This article investigates global exponential stabilization (GES) of Takagi-Sugeno (T-S) fuzzy memristive neural networks with multiple time-varying delays (DFMNNs) via intermittent control strategy. By resorting to differential inclusion theory, comparison means, and inequality techniques, some results are developed to ensure GES of the underlying DFMNNs via a fuzzy intermittent state feedback control law within the sense of Filippov. The outcome is generalized to GES of FMNNs with infinite distributed time delays. Additionally, the global exponential stability of FMNNs with discrete time-varying delays is explored in terms of 1-norm. The derived conditions herein contain certain existing ones as special cases. Finally, three examples are presented to illuminate the validness of the outcomes.
引用
收藏
页码：3092 / 3101
页数：10
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