Novel Finite-Time Reliable Control Design for Memristor-Based Inertial Neural Networks With Mixed Time-Varying Delays

被引:97
|
作者
Hua, Lanfeng [1 ]
Zhu, Hong [1 ]
Shi, Kaibo [2 ]
Zhong, Shouming [3 ]
Tang, Yiqian [2 ]
Liu, Yajuan [4 ]
机构
[1] Univ Elect Sci & Technol China, Sch Automat Engn, Chengdu 611731, Peoples R China
[2] Chengdu Univ, Sch Informat Sci & Engn, Chengdu 610106, Peoples R China
[3] Univ Elect Sci & Technol China, Sch Math Sci, Chengdu 611731, Peoples R China
[4] North China Elect Power Univ, Sch Control & Comp Engn, Beijing 102206, Peoples R China
基金
中国国家自然科学基金; 中国博士后科学基金;
关键词
Reliability; Delays; Reliability engineering; Artificial neural networks; Stability analysis; Reliability theory; Circuit stability; Finite-time stabilization; memristor-based inertial neural networks; new analytical method; mixed time-varying delays; reliable control design;
D O I
10.1109/TCSI.2021.3052210
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
The issue of finite-time stabilization (FTS) for the memristor-based inertial neural networks (MINNs) with mixed time-varying delays (MTVDs) is researched by virtue of a new analytical method in this brief. First, an appropriate reliable control strategy is proposed for MINNs, which takes the influence of actuator failures into account. Second, by combining Lyapunov functional theory with new analysis techniques, novel theoretical results to guarantee the FTS for the concerned MINNs are acquired, and the desired reliable controller gains are obtained simultaneously. In additions, compared with the previous research works, the FTS results obtained in this paper are established directly from the MINNs themselves without using variable transformation method. In the end, two simulations are exploited to show the correctness and practicability of the acquired theoretical results.
引用
收藏
页码:1599 / 1609
页数:11
相关论文
共 50 条
  • [1] New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays
    Wang, Jun
    Tian, Yongqiang
    Hua, Lanfeng
    Shi, Kaibo
    Zhong, Shouming
    Wen, Shiping
    MATHEMATICS, 2023, 11 (03)
  • [2] Finite-Time Synchronization of Memristor-Based Recurrent Neural Networks With Inertial Items and Mixed Delays
    Lu, Zhenyu
    Ge, Quanbo
    Li, Yan
    Hu, Junhao
    IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2021, 51 (05): : 2701 - 2711
  • [3] Finite-time stabilization of memristor-based inertial neural networks with time-varying delays combined with interval matrix method
    Wei, Fei
    Chen, Guici
    Wang, Wenbo
    KNOWLEDGE-BASED SYSTEMS, 2021, 230 (230)
  • [4] Finite-Time Synchronization of Multi-Linked Memristor-Based Neural Networks With Mixed Time-Varying Delays
    Wang, Shaofang
    Li, Lixiang
    Peng, Haipeng
    Yang, Yixian
    Zheng, Mingwen
    IEEE ACCESS, 2020, 8 (08): : 169966 - 169981
  • [5] Global finite-time stabilization of memristor-based neural networks with time-varying delays via hybrid control
    Song, Yinfang
    PROCEEDINGS OF THE 36TH CHINESE CONTROL CONFERENCE (CCC 2017), 2017, : 896 - 903
  • [6] Adaptive finite-time synchronization of stochastic mixed time-varying delayed memristor-based neural networks
    Zhang, Tianliang
    Deng, Feiqi
    NEUROCOMPUTING, 2021, 452 : 781 - 788
  • [7] Finite-Time Stabilization of Competitive Neural Networks With Time-Varying Delays
    Sheng, Yin
    Zeng, Zhigang
    Huang, Tingwen
    IEEE TRANSACTIONS ON CYBERNETICS, 2022, 52 (11) : 11325 - 11334
  • [8] Global dissipativity and finite-time synchronization of mixed time-varying delayed memristor-based neural networks with discontinuous activations
    Fei, Kaifang
    Jiang, Minghui
    Zhang, Yadan
    JOURNAL OF INTELLIGENT & FUZZY SYSTEMS, 2021, 40 (01) : 1695 - 1712
  • [9] On synchronization for chaotic memristor-based neural networks with time-varying delays
    Zheng, Cheng-De
    Xian, Yongjin
    NEUROCOMPUTING, 2016, 216 : 570 - 586
  • [10] State estimation for memristor-based neural networks with time-varying delays
    Wei, Hongzhi
    Li, Ruoxia
    Chen, Chunrong
    INTERNATIONAL JOURNAL OF MACHINE LEARNING AND CYBERNETICS, 2015, 6 (02) : 213 - 225