Finite-time stabilization of complex-valued neural networks with proportional delays and inertial terms: A non-separation approach

被引:43
作者
Long, Changqing [1 ]
Zhang, Guodong [2 ]
Zeng, Zhigang [3 ]
Hu, Junhao [2 ]
机构
[1] Jishou Univ, Sch Math & Stat, Jishou 416000, Peoples R China
[2] South Cent Univ Nationalities, Sch Math & Stat, Wuhan 430074, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
基金
美国国家科学基金会;
关键词
Finite-time stabilization; Inertial terms; Proportional delays; Complex-valued neural networks; Lyapunov functions; NON-REDUCED ORDER; EXPONENTIAL STABILITY; ADAPTIVE-CONTROL; VARYING DELAYS; STATE-FEEDBACK; SYNCHRONIZATION; DYNAMICS;
D O I
10.1016/j.neunet.2022.01.005
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 mainly dedicates on the issue of finite-time stabilization of complex-valued neural networks with proportional delays and inertial terms via directly constructing Lyapunov functions without separating the original complex-valued neural networks into two real-valued subsystems equivalently. First of all, in order to facilitate the analysis of the second-order derivative caused by the inertial term, two intermediate variables are introduced to transfer complex-valued inertial neural networks (CVINNs) into the first-order differential equation form. Then, under the finite-time stability theory, some new criteria with less conservativeness are established to ensure the finite-time stabilizability of CVINNs by a newly designed complex-valued feedback controller. In addition, for reducing expenses of the control, an adaptive control strategy is also proposed to achieve the finite time stabilization of CVINNs. At last, numerical examples are given to demonstrate the validity of the derived results.(C) 2022 Elsevier Ltd. All rights reserved.
引用
收藏
页码:86 / 95
页数:10
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