Finite-Time Stabilization and Adaptive Control of Memristor-Based Delayed Neural Networks

被引：110
作者：
Wang, Leimin [1 ]
Shen, Yi [2 ]
Zhang, Guodong [3 ]
机构：
[1] China Univ Geosci, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[2] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[3] South Cent Univ Nationalities, Coll Math & Stat, Wuhan 430074, Hubei, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2017年 / 28卷 / 11期
基金：
中国国家自然科学基金; 美国国家科学基金会;
关键词：
Finite-time stabilization; memristor-based delayed neural networks; delayed state feedback control; adaptive control; GLOBAL EXPONENTIAL STABILITY; VARYING DELAYS; NONLINEAR-SYSTEMS; INTERMITTENT CONTROL; SWITCHING DESIGN; FEEDBACK-CONTROL; SYNCHRONIZATION; DISCRETE; PERIODICITY;
D O I：
10.1109/TNNLS.2016.2598598
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Finite-time stability problem has been a hot topic in control and system engineering. This paper deals with the finite-time stabilization issue of memristor-based delayed neural networks (MDNNs) via two control approaches. First, in order to realize the stabilization of MDNNs in finite time, a delayed state feedback controller is proposed. Then, a novel adaptive strategy is applied to the delayed controller, and finite-time stabilization of MDNNs can also be achieved by using the adaptive control law. Some easily verified algebraic criteria are derived to ensure the stabilization of MDNNs in finite time, and the estimation of the settling time functional is given. Moreover, several finite-time stability results as our special cases for both memristor-based neural networks (MNNs) without delays and neural networks are given. Finally, three examples are provided for the illustration of the theoretical results.
引用
收藏
页码：2648 / 2659
页数：12
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