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Further results on fixed/preassigned-time projective lag synchronization control of hybrid inertial neural networks with time delays
被引:16
|作者:
Zhang, Guodong
[1
]
Cao, Jinde
[2
,3
]
Kashkynbayev, Ardak
[4
]
机构:
[1] South Cent Minzu Univ, Sch Math & Stat, Wuhan 430074, Peoples R China
[2] Southeast Univ, Sch Math, Nanjing 210096, Peoples R China
[3] Yonsei Univ, Yonsei Frontier Lab, Seoul 03722, South Korea
[4] Nazarbayev Univ, Dept Math, Nur Sultan 010000, Kazakhstan
来源:
基金:
中国国家自然科学基金;
关键词:
Fixed-time projective lag synchronization;
Preassigned-time lag projective synchronization;
Hybrid inertial neural networks;
Time delays;
FINITE-TIME;
DYNAMICAL-SYSTEMS;
STABILITY;
ORDER;
MEMRISTOR;
MODELS;
MEMORY;
STABILIZATION;
D O I:
10.1016/j.jfranklin.2023.07.040
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 aims to study fixed-time projective lag synchronization(FXPLS) and preassigned-time projective lag synchronization(PTPLS) of hybrid inertial neural networks(HINNs) with state-switched and discontinuous activation functions(DAFs). By constructing new hybrid fixed-time control and based on theory of non-smooth analysis, we achieve novel results on FXPLS for such HINNs. Through designing novel hybrid preassigned-time control, new criteria on PTPLS of the HINNs is also taken into account. And as distinct from recent works, the FXPLS and PTPLS results are established via non-variable substitution and in a more generalized framework than common synchronization, which also has more extensive practical applications. Finally, example simulations are displayed to set forth the validity of the acquired FXPLS and PTPLS. & COPY; 2023 The Franklin Institute. Published by Elsevier Inc. All rights reserved.
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页码:9950 / 9973
页数:24
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