Event-based passification of delayed memristive neural networks

被引:3
|
作者
Cao, Yuting [1 ]
Wang, Shiqin [2 ]
Guo, Zhenyuan [1 ]
Huang, Tingwen [3 ]
Wen, Shiping [2 ]
机构
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Peoples R China
[2] Univ Technol Sydney, Australian AI Inst, Sydney, NSW 2007, Australia
[3] Texas A&M Univ Qatar, Sci Program, Doha 23874, Qatar
关键词
Time-varying delay; Passification; Event-based algorithm; TIME-VARYING DELAYS; EXPONENTIAL SYNCHRONIZATION; STABILITY; PASSIVITY; SYSTEM;
D O I
10.1016/j.ins.2021.03.045
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
As well known, neural network is a mathematical model obtained by simulating biological synapse for information processing [1,2]. Memristor was first predicted by Chua in [3]. Since the successful establishment of the physical model of memristor in 2008 [4], scientists have been enthusiastic about the dynamics of memristive neural networks [5]. As a kind of nonlinear resistor with memory function, the non-linear nature of memristor can generate chaotic circuits with rich applications in secure communications [6]. In the circuit implementation of neural networks, memristors can also be used to replace resistors to describe the connection weights as state-dependent functions in the mathematical model, then we can get the memristive neural networks [7]. In addition, the dynamic behaviors of memristive neural networks has also attracted widespread attention [8-10]. In the practical application of neural networks, due to the switching speed of amplifiers, the transmission speed of elec This paper focuses on the passification issue of delayed memristive neural networks via the event-based control. First, by designing an appropriate controller based on a static event trigger scheme, the passification conditions are deduced for delayed memristive neural networks. Then, under the same controller, the passivity is discussed for the delayed memristive neural network system with a more economical and realistic dynamic event trigger rule. Meanwhile, in order to ensure these two event trigger control schemes are Zeno free, the existence of positive lower bounds are approved for the inter event time. Finally, illustrative examples are elaborated to support the theoretical results. (c) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页码:344 / 357
页数:14
相关论文
共 50 条
  • [21] Denoising of event-based sensors with deep neural networks
    Zhang, Zhihong
    Suo, Jinli
    Dai, Qionghai
    OPTOELECTRONIC IMAGING AND MULTIMEDIA TECHNOLOGY VIII, 2021, 11897
  • [22] Event-based nonfragile state estimation for memristive recurrent neural networks with stochastic cyber-attacks and sensor saturations
    Shao, Xiao-Guang
    Zhang, Jie
    Lu, Yan-Juan
    CHINESE PHYSICS B, 2024, 33 (07)
  • [23] Stabilization of memristive neural networks with mixed time-varying delays via continuous/periodic event-based control
    Cao, Yuting
    Wang, Shiqin
    Guo, Zhenyuan
    Huang, Tingwen
    Wen, Shiping
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2020, 357 (11): : 7122 - 7138
  • [24] Event-Based Impulsive Control of Continuous-Time Dynamic Systems and Its Application to Synchronization of Memristive Neural Networks
    Zhu, Wei
    Wang, Dandan
    Liu, Lu
    Feng, Gang
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (08) : 3599 - 3609
  • [25] Pinning Event-Triggered Scheme for Synchronization of Delayed Uncertain Memristive Neural Networks
    Fan, Jiejie
    Ban, Xiaojuan
    Yuan, Manman
    Zhang, Wenxing
    MATHEMATICS, 2024, 12 (06)
  • [26] Passivity and passification of memristive neural networks with leakage term and time-varying delays
    Wang, Shengbo
    Cao, Yanyi
    Huang, Tingwen
    Wen, Shiping
    APPLIED MATHEMATICS AND COMPUTATION, 2019, 361 : 294 - 310
  • [27] Synchronization of stochastic delayed dynamical networks with directed event-based couplings
    Jia, Qiang
    Liu, Zirong
    Jiao, Ticao
    Cai, Shuiming
    PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS, 2025, 658
  • [28] Passivity and passification of memristive recurrent neural networks with multi-proportional delays and impulse
    Wang, Yuxiao
    Cao, Yuting
    Guo, Zhenyuan
    Wen, Shiping
    APPLIED MATHEMATICS AND COMPUTATION, 2020, 369 (369)
  • [29] Pinning Stabilization of Connected Neural Networks with Event-based Couplings
    Huang, Chi
    Li, Lulu
    Lu, Jianquan
    2014 INTERNATIONAL CONFERENCE ON MECHATRONICS AND CONTROL (ICMC), 2014, : 2464 - 2469
  • [30] Periodic synchronization in delayed memristive neural networks based on Filippov systems
    Cai, Zuowei
    Huang, Lihong
    Wang, Dongshu
    Zhang, Lingling
    JOURNAL OF THE FRANKLIN INSTITUTE-ENGINEERING AND APPLIED MATHEMATICS, 2015, 352 (10): : 4638 - 4663