Finite-Time Stabilization of Inertial Memristive Neural Networks via Nonreduced Order Method

被引：0
|
作者：
Zhang, Jun [1 ]
Zhu, Song [1 ]
Liu, Xiaoyang [2 ]
Wen, Shiping [3 ]
Mu, Chaoxu [4 ]
机构：
[1] China Univ Min & Technol, Sch Math, JCAM, Xuzhou 221116, Peoples R China
[2] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
[3] Univ Technol Sydney, Ctr Artificial Intelligence, Ultimo, NSW 2007, Australia
[4] Tianjin Univ, Sch Elect & Automat Engn, Tianjin 300072, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2024年
基金：
中国国家自然科学基金;
关键词：
Finite-time stabilization; inertial memristive neural networks (IMNNs); nonreduced order method; unbounded time-varying delays; STABILITY; DIFFUSION; DYNAMICS;
D O I：
10.1109/TNNLS.2024.3422655
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This article investigates the finite-time stabilization problem of inertial memristive neural networks (IMNNs) with bounded and unbounded time-varying delays, respectively. To simplify the theoretical derivation, the nonreduced order method is utilized for constructing appropriate comparison functions and designing a discontinuous state feedback controller. Then, based on the controller, the state of IMNNs can directly converge to 0 in finite time. Several criteria for finite-time stabilization of IMNNs are obtained and the setting time is estimated. Compared with previous studies, the requirement of differentiability of time delay is eliminated. Finally, numerical examples illustrate the usefulness of the analysis results in this article.
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页数：11
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