Synchronization of fractional-order delayed coupled networks with reaction-diffusion terms and Neumann boundary value conditions

被引:1
作者
Zhang, Shuailei [1 ]
Liu, Xinge [1 ]
Ullah, Saeed [1 ]
Tang, Meilan [1 ]
Xu, Hongfu [1 ]
机构
[1] Cent South Univ, Sch Math & Stat, Changsha 410083, Hunan, Peoples R China
来源
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION | 2024年 / 129卷
基金
中国国家自然科学基金;
关键词
Fractional-order coupled network; Reaction-diffusion terms; Synchronization; Adaptive pinning control; GLOBAL EXPONENTIAL STABILITY; TIME OUTPUT SYNCHRONIZATION; NEURAL-NETWORKS; CLUSTER SYNCHRONIZATION; CONTROL STRATEGIES; ADAPTIVE-CONTROL; VARYING DELAYS;
D O I
10.1016/j.cnsns.2023.107696
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper investigates the adaptive pinning strategies for achieving synchronization of fractional-order delayed coupled networks with reaction-diffusion terms and a digraph topology by incorporating Neumann boundary value conditions. By employing the inf-sup method, a novel fractional-order inequality is proved. The classical Poincare inequality is also extended by utilizing the Holder inequality. Two types of control laws are developed to achieve synchroniza-tion: one with control gains dependent solely on time, and another with control gains dependent on both space and time. For each case, adaptive control laws and synchronization criteria based on matrix inequalities are proposed. Finally, the effectiveness of the synchronization results is demonstrated through two numerical examples.
引用
收藏
页数:20
相关论文
共 50 条
[1]   Adaptive Synchronization of Fractional-Order Output-Coupling Neural Networks via Quantized Output Control [J].
Bao, Haibo ;
Park, Ju H. ;
Cao, Jinde .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2021, 32 (07) :3230-3239
[2]   Application of a fractional advection-dispersion equation [J].
Benson, DA ;
Wheatcraft, SW ;
Meerschaert, MM .
WATER RESOURCES RESEARCH, 2000, 36 (06) :1403-1412
[3]   Mittag-Leffler stabilization for coupled fractional reaction-diffusion neural networks subject to boundary matched disturbance [J].
Cai, Rui-Yang ;
Kou, Chun-Hai .
MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2023, 46 (03) :3143-3156
[4]   Adaptive quasi-synchronization control of heterogeneous fractional-order coupled neural networks with reaction-diffusion [J].
Chen, Wei ;
Yu, Yongguang ;
Hai, Xudong ;
Ren, Guojian .
APPLIED MATHEMATICS AND COMPUTATION, 2022, 427
[5]   CELLULAR NEURAL NETWORKS - THEORY [J].
CHUA, LO ;
YANG, L .
IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS, 1988, 35 (10) :1257-1272
[6]   Nondiffusive transport in plasma turbulence: A fractional diffusion approach [J].
del-Castillo-Negrete, D ;
Carreras, BA ;
Lynch, VE .
PHYSICAL REVIEW LETTERS, 2005, 94 (06)
[7]  
Diethelm K., 2010, LECT NOTES MATH
[8]   Synchronization of Coupled Neural Networks via an Event-Dependent Intermittent Pinning Control [J].
Ding, Sanbo ;
Wang, Zhanshan .
IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2022, 52 (03) :1928-1934
[9]   Comparison principle and synchronization analysis of fractional-order complex networks with parameter uncertainties and multiple time delays [J].
Fan, Hongguang ;
Zhu, Jihong ;
Wen, Hui .
AIMS MATHEMATICS, 2022, 7 (07) :12981-12999
[10]  
Feng Wang, 2012, Proceedings of the 2012 32nd International Conference on Distributed Computing Systems Workshops (ICDCS Workshops), P133, DOI 10.1109/ICDCSW.2012.16