Finite-time synchronization of quaternion-valued neural networks with delays: A switching control method without decomposition

被引：33

作者：

Peng, Tao ^{[1
,2
]}

Zhong, Jie ^{[3
]}

Tu, Zhengwen ^{[2
]}

Lu, Jianquan ^{[1
]}

Lou, Jungang ^{[4
]}

机构：

[1] Southeast Univ, Sch Math, Dept Syst Sci, Nanjing 210096, Peoples R China

[2] Chongqing Three Gorges Univ, Sch Math & Stat, Wanzhou 404100, Peoples R China

[3] Zhejiang Normal Univ, Coll Math & Comp Sci, Jinhua 321004, Zhejiang, Peoples R China

[4] Huzhou Univ, Sch Informat Engn, Zhejiang Prov Key Lab Smart Management & Applicat, Huzhou 313000, Peoples R China

来源：

NEURAL NETWORKS | 2022年 / 148卷

基金：

中国国家自然科学基金;

关键词：

Finite-time synchronization; Quaternion-valued neural networks; Time delays; Switching control method without; decomposition; EXPONENTIAL STABILITY; ANTI-SYNCHRONIZATION; SYSTEMS; STABILIZATION; PARAMETERS; DISCRETE;

D O I：

10.1016/j.neunet.2021.12.012

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Fora class of quaternion-valued neural networks (QVNNs) with discrete and distributed time delays, its finite-time synchronization (FTSYN) is addressed in this paper. Instead of decomposition, a direct analytical method named two-step analysis is proposed. That method can always be used to study FTSYN, under either 1-norm or 2-norm of quaternion. Compared with the decomposing method, the two-step method is also suitable for models that are not easily decomposed. Furthermore, a switching controller based on the two-step method is proposed. In addition, two criteria are given to realize the FTSYN of QVNNs. At last, three numerical examples illustrate the feasibility, effectiveness and practicability of our method.(c) 2021 Elsevier Ltd. All rights reserved.

引用

页码：37 / 47

页数：11

共 50 条

[21] Finite-time synchronization of complex-valued neural networks with finite-time distributed delays
Liu, Yanjun
Huang, Junjian
Qin, Yu
Yang, Xinbo
NEUROCOMPUTING, 2020, 416 : 152 - 157
[22] Finite-time synchronization for fractional-order quaternion-valued coupled neural networks with saturated impulse
Mo, Wenjun
Bao, Haibo
CHAOS SOLITONS & FRACTALS, 2022, 164
[23] Global exponential stability for quaternion-valued neural networks with time-varying delays by matrix measure method
Chen, Yifeng
Shi, Yanchao
Guo, Jun
Cai, Jingling
COMPUTATIONAL & APPLIED MATHEMATICS, 2025, 44 (01)
[24] Finite-Time Synchronization of Fractional-Order Quaternion-Valued Delayed Cohen-Grossberg Neural Networks
Zhongwen Wu
Xiaobing Nie
Neural Processing Letters, 2023, 55 : 12255 - 12271
[25] Stability of Quaternion-Valued Neural Networks with Mixed Delays
Zhu, Jingwen
Sun, Jitao
NEURAL PROCESSING LETTERS, 2019, 49 (02) : 819 - 833
[26] Finite-time stabilization of fractional-order fuzzy quaternion-valued BAM neural networks via direct quaternion approach
Chen, Shenglong
Li, Hong-Li
Kao, Yonggui
Zhang, Long
Hu, Cheng
JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2021, 358 (15): : 7650 - 7673
[27] Finite-Time Synchronization of Fractional-Order Quaternion-Valued Delayed Cohen-Grossberg Neural Networks
Wu, Zhongwen
Nie, Xiaobing
NEURAL PROCESSING LETTERS, 2023, 55 (09) : 12255 - 12271
[28] Synchronization in Finite-Time Analysis of Clifford-Valued Neural Networks with Finite-Time Distributed Delays
Rajchakit, Grienggrai
Sriraman, Ramalingam
Lim, Chee Peng
Sam-ang, Panu
Hammachukiattikul, Porpattama
MATHEMATICS, 2021, 9 (11)
[29] Synchronization of Quaternion Valued Neural Networks with Mixed Time Delays Using Lyapunov Function Method
Singh, Sunny
Kumar, Umesh
Das, Subir
Alsaadi, F.
Cao, Jinde
NEURAL PROCESSING LETTERS, 2022, 54 (02) : 785 - 801
[30] Almost periodic synchronization of quaternion-valued fuzzy cellular neural networks with leakage delays
Wang, Pan
Li, Xuechen
Wang, Nan
Li, Yuanyuan
Shi, Kaibo
Lu, Jianquan
FUZZY SETS AND SYSTEMS, 2022, 426 : 46 - 65

← 1 2 3 4 5 →