Fixed-Time Synchronization and Energy Consumption Prediction of Interconnected Memristive Neural Networks with Discontinuous Activation Functions

被引：0
|
作者：
Wang, Xuan [1 ]
Tong, Dongbing [1 ]
Chen, Qiaoyu [1 ]
Zhou, Wuneng [2 ]
机构：
[1] Shanghai Univ Engn Sci, Sch Elect & Elect Engn, Shanghai 201620, Peoples R China
[2] Donghua Univ, Coll Informat Sci & Technol, Shanghai 200051, Peoples R China
来源：
CIRCUITS SYSTEMS AND SIGNAL PROCESSING | 2024年 / 43卷 / 11期
关键词：
Fixed-time synchronization; Interconnected memristive neural networks; Energy consumption; Discontinuous activation functions; SYSTEMS;
D O I：
10.1007/s00034-024-02806-y
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In this paper, the fixed-time synchronization (FXTS) and energy consumption prediction of interconnected memristive neural networks (IMNNs) with discontinuous activation functions are studied. First of all, to deal with discontinuous activation functions, a unified discontinuous controller, which can be applied to other networks, is designed to effectuate FXTS. Based on Lyapunov stability theory, some sufficient conditions for the synchronization of IMNNs are given, and the settling-time is estimated. Meanwhile, to accurately evaluate engineering costs, the energy consumption of the system control process is obtained, and the influence of specific controller parameters on energy consumption is analyzed. Finally, a numerical simulation is presented to verify the correctness of the results.
引用
收藏
页码：6977 / 6995
页数：19
相关论文
共 50 条
  • [31] Finite-time synchronization of memristive neural networks with discontinuous activation functions and mixed time-varying delays
    Li, Xiaofan
    Zhang, Wenbing
    Fang, Jian-an
    Li, Huiyuan
    NEUROCOMPUTING, 2019, 340 : 99 - 109
  • [32] Finite-time synchronization of fractional-order memristive recurrent neural networks with discontinuous activation functions
    Li, Xiaofan
    Fang, Jian-an
    Zhang, Wenbing
    Li, Huiyuan
    NEUROCOMPUTING, 2018, 316 : 284 - 293
  • [33] Synchronization of Interconnected Discontinuous Neural Networks With Nonlinear Coupling Functions
    Wang, Zengyun
    Liu, Xinzhi
    IEEE ACCESS, 2019, 7 : 25804 - 25814
  • [34] An Improved Finite-Time and Fixed-Time Stable Synchronization of Coupled Discontinuous Neural Networks
    Xiao, Qizhen
    Liu, Hongliang
    Wang, Yinkun
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, 34 (07) : 3516 - 3526
  • [35] A Unified Framework Design for Finite-Time and Fixed-Time Synchronization of Discontinuous Neural Networks
    Xiao, Jian
    Zeng, Zhigang
    Wen, Shiping
    Wu, Ailong
    Wang, Leimin
    IEEE TRANSACTIONS ON CYBERNETICS, 2021, 51 (06) : 3004 - 3016
  • [36] Controller design for global fixed-time synchronization of delayed neural networks with discontinuous activations
    Wang, Leimin
    Zeng, Zhigang
    Hu, Junhao
    Wang, Xiaoping
    NEURAL NETWORKS, 2017, 87 : 122 - 131
  • [37] Output fixed-time synchronization of discontinuous coupled neural networks with multiple output couplings
    Wei, Ruoyu
    Cao, Jinde
    Duan, Peiyong
    Gorbachev, Sergey
    MATHEMATICAL METHODS IN THE APPLIED SCIENCES, 2022, 45 (16) : 9753 - 9768
  • [38] Fixed-time synchronization analysis for discontinuous fuzzy inertial neural networks with parameter uncertainties
    Kong, Fanchao
    Zhu, Quanxin
    Sakthivel, Rathinasamy
    Mohammadzadeh, Ardashir
    NEUROCOMPUTING, 2021, 422 : 295 - 313
  • [39] Fixed-time synchronization of discontinuous fuzzy competitive neural networks via quantized control
    Zheng, Caicai
    Yu, Juan
    Kong, Fanchao
    Hu, Cheng
    FUZZY SETS AND SYSTEMS, 2024, 482
  • [40] Fixed-time synchronization of discontinuous proportional delay inertial neural networks with uncertain parameters
    Wan, Yan
    Zhou, Liqun
    INFORMATION SCIENCES, 2024, 678
12345