Finite/fixed-time synchronization of memristive neural networks via event-triggered control

被引：21
作者：
Ping, Jing [1 ]
Zhu, Song [1 ]
Liu, Xiaoyang [2 ]
机构：
[1] China Univ Min & Technol, Sch Math, Xuzhou 221116, Peoples R China
[2] Jiangsu Normal Univ, Sch Comp Sci & Technol, Xuzhou 221116, Peoples R China
来源：
KNOWLEDGE-BASED SYSTEMS | 2022年 / 258卷
基金：
中国国家自然科学基金;
关键词：
Finite; fixed-time synchronization; Event-triggered control; Memristive neural networks; Zeno behavior; EXPONENTIAL SYNCHRONIZATION; DELAY; PARAMETERS; STABILITY;
D O I：
10.1016/j.knosys.2022.110013
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
By applying event-triggered control schemes, this paper investigates the issues of finite/fixed-time synchronization for a class of memristive neural networks (MNNs). In order to reduce the transmission burden and realize a fast rate of synchronization, the appropriate controller and the measurement error function are designed. Then, by using Lyapunov stability theory and inequality techniques, some efficient criteria are proposed to guarantee the finite/fixed-time synchronization, respectively. Besides, the estimations of the synchronization time are derived, and the Zeno phenomenon can be excluded under the derived event-triggered schemes. Finally, simulations are provided to verify the obtained results.(c) 2022 Elsevier B.V. All rights reserved.
引用
收藏
页数：9
相关论文
共 49 条
[1]   Finite-time synchronization for memristor-based neural networks with time-varying delays [J].
Abdurahman, Abdujelil ;
Jiang, Haijun ;
Teng, Zhidong .
NEURAL NETWORKS, 2015, 69 :20-28
[2]   Exponential Synchronization of Coupled Stochastic Memristor-Based Neural Networks With Time-Varying Probabilistic Delay Coupling and Impulsive Delay [J].
Bao, Haibo ;
Park, Ju H. ;
Cao, Jinde .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2016, 27 (01) :190-201
[3]  
Cao Y., 2022, KNOWL-BASED SYST
[4]   Synchronization of memristive neural networks with leakage delay and parameters mismatch via event-triggered control [J].
Cao, Yuting ;
Wang, Shengbo ;
Guo, Zhenyuan ;
Huang, Tingwen ;
Wen, Shiping .
NEURAL NETWORKS, 2019, 119 :178-189
[5]   Synchronization of memristor-based recurrent neural networks with two delay components based on second-order reciprocally convex approach [J].
Chandrasekar, A. ;
Rakkiyappan, R. ;
Cao, Jinde ;
Lakshmanan, S. .
NEURAL NETWORKS, 2014, 57 :79-93
[6]   Predefined-time synchronization of competitive neural networks [J].
Chen, Chuan ;
Mi, Ling ;
Liu, Zhongqiang ;
Qiu, Baolin ;
Zhao, Hui ;
Xu, Lijuan .
NEURAL NETWORKS, 2021, 142 :492-499
[7]   Exponential quasi-synchronization of coupled delayed memristive neural networks via intermittent event-triggered control [J].
Chen, Jiejie ;
Chen, Boshan ;
Zeng, Zhigang .
NEURAL NETWORKS, 2021, 141 (141) :98-106
[8]   Global Uniform Asymptotic Fixed Deviation Stability and Stability for Delayed Fractional-order Memristive Neural Networks with Generic Memductance [J].
Chen, Jiejie ;
Chen, Boshan ;
Zeng, Zhigang .
NEURAL NETWORKS, 2018, 98 :65-75
[9]   Event-Based Output Quantized Synchronization Control for Multiple Delayed Neural Networks [J].
Chen, Yue ;
Zhu, Song ;
Shen, Mouquan ;
Liu, Xiaoyang ;
Wen, Shiping .
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2024, 35 (01) :428-438
[10]   MEMRISTOR - MISSING CIRCUIT ELEMENT [J].
CHUA, LO .
IEEE TRANSACTIONS ON CIRCUIT THEORY, 1971, CT18 (05) :507-+
12345