Finite-Time Passivity for Coupled Fractional-Order Neural Networks With Multistate or Multiderivative Couplings

被引:16
作者
Liu, Chen-Guang [1 ]
Wang, Jin-Liang [2 ,3 ]
Wu, Huai-Ning [4 ,5 ]
机构
[1] Tiangong Univ, Sch Comp Sci & Technol, Tianjin 300387, Peoples R China
[2] Tiangong Univ, Sch Comp Sci & Technol, Tianjin Key Lab Autonomous Intelligence Technol &, Tianjin 300387, Peoples R China
[3] Linyi Univ, Sch Informat Sci & Technol, Linyi 276005, Shandong, Peoples R China
[4] Beihang Univ, Sch Automat Sci & Elect Engn, Sci & Technol Aircraft Control Lab, Beijing 100191, Peoples R China
[5] Peng Cheng Lab, Shenzhen 518000, Peoples R China
基金
中国国家自然科学基金;
关键词
Couplings; Synchronization; Neural networks; Adaptive systems; Symmetric matrices; Stability criteria; Numerical stability; Coupled fractional-order neural networks (CFNNs); finite-time passivity (FTP); multiderivative couplings; multistate couplings; STABILITY ANALYSIS; STABILIZATION;
D O I
10.1109/TNNLS.2021.3132069
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 delves into the finite-time passivity (FTP) for coupled fractional-order neural networks with multistate couplings (CFNNMSCs) or coupled fractional-order neural networks with multiderivative couplings (CFNNMDCs). Distinguishing from the traditional FTP definitions, several concepts of FTP for fractional-order systems are given. On one hand, we present several sufficient conditions to ensure the FTP for CFNNMSCs by artfully designing a state-feedback controller and an adaptive state-feedback controller. On the other hand, by utilizing some inequality techniques, two sets of FTP criteria for CFNNMDCs are also established on the basis of the state-feedback and adaptive state-feedback controllers. Finally, numerical examples are used to demonstrate the validity of the derived FTP criteria.
引用
收藏
页码:5976 / 5987
页数:12
相关论文
共 50 条
[31]   Finite-time synchronization for fractional-order quaternion-valued coupled neural networks with saturated impulse [J].
Mo, Wenjun ;
Bao, Haibo .
CHAOS SOLITONS & FRACTALS, 2022, 164
[32]   Finite-Time Synchronization of Fractional-Order Fuzzy Time-Varying Coupled Neural Networks Subject to Reaction-Diffusion [J].
Xu, Yao ;
Liu, Wenxi ;
Wu, Yongbao ;
Li, Wenxue .
IEEE TRANSACTIONS ON FUZZY SYSTEMS, 2023, 31 (10) :3423-3432
[33]   Finite-time synchronization criterion of graph theory perspective fractional-order coupled discontinuous neural networks [J].
Pratap, A. ;
Raja, R. ;
Cao, Jinde ;
Alzabut, J. ;
Huang, Chuangxia .
ADVANCES IN DIFFERENCE EQUATIONS, 2020, 2020 (01)
[34]   Finite-Time Synchronization and Energy Consumption Prediction for Multilayer Fractional-Order Networks [J].
Tong, Dongbing ;
Ma, Ben ;
Chen, Qiaoyu ;
Wei, Yunbing ;
Shi, Peng .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS, 2023, 70 (06) :2176-2180
[35]   Finite-time stability of fractional-order fuzzy cellular neural networks with time delays [J].
Du, Feifei ;
Lu, Jun-Guo .
FUZZY SETS AND SYSTEMS, 2022, 438 :107-120
[36]   Finite-time stability criteria for a class of fractional-order neural networks with delay [J].
Chen, Liping ;
Liu, Cong ;
Wu, Ranchao ;
He, Yigang ;
Chai, Yi .
NEURAL COMPUTING & APPLICATIONS, 2016, 27 (03) :549-556
[37]   Finite-time synchronization of fractional-order memristive recurrent neural networks with discontinuous activation functions [J].
Li, Xiaofan ;
Fang, Jian-an ;
Zhang, Wenbing ;
Li, Huiyuan .
NEUROCOMPUTING, 2018, 316 :284-293
[38]   Finite-time non-fragile control for synchronization of fractional-order stochastic neural networks [J].
Kanakalakshmi, S. ;
Sakthivel, R. ;
Karthick, S. A. ;
Wang, Chao ;
Leelamani, A. .
SOFT COMPUTING, 2023, 27 (05) :2453-2463
[39]   The Passivity of Uncertain Fractional-Order Neural Networks with Time-Varying Delays [J].
Xu, Song ;
Liu, Heng ;
Han, Zhimin .
FRACTAL AND FRACTIONAL, 2022, 6 (07)
[40]   Finite-Time Passivity of Stochastic Coupled Complex Networks [J].
Yin, Xunwu ;
Cao, Min .
DISCRETE DYNAMICS IN NATURE AND SOCIETY, 2021, 2021