Exponential stability analysis of delayed memristor-based recurrent neural networks with impulse effects

被引：0
|
作者：
Huamin Wang
Shukai Duan
Chuandong Li
Lidan Wang
Tingwen Huang
机构：
[1] Southwest University,College of Electronic and Information Engineering
[2] Luoyang Normal University,Department of Mathematics
[3] Texas A&M University at Qatar,Department of Electrical and Computer Engineering
来源：
Neural Computing and Applications | 2017年 / 28卷
关键词：
Memristor-based recurrent neural networks; Exponential stability; Impulse effects; Impulsive differential inequality;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
In this paper, a generalized memristor-based recurrent neural network model with variable delays and impulse effects is considered. By using an impulsive delayed differential inequality and Lyapunov function, the exponential stability of the impulsive delayed memristor-based recurrent neural networks is investigated. Several exponential and uniform stability criteria of this impulsive delayed system are derived, which promotes the study of memristor-based recurrent neural networks. Finally, the effectiveness of obtained results is illustrated by two numerical examples.
引用
收藏
页码：669 / 678
页数：9
相关论文
共 50 条
  • [21] New criteria for exponential stability of delayed recurrent neural networks
    Xiao, Jian
    Zeng, Zhigang
    Wu, Ailong
    NEUROCOMPUTING, 2014, 134 : 182 - 188
  • [22] Multiple μ-stability and multiperiodicity of delayed memristor-based fuzzy cellular neural networks with nonmonotonic activation functions
    Liu, Yunfeng
    Song, Zhiqiang
    Tan, Manchun
    MATHEMATICS AND COMPUTERS IN SIMULATION, 2019, 159 : 1 - 17
  • [23] Exponential stability and synchronization of Memristor-based fractional-order fuzzy cellular neural networks with multiple delays
    Yao, Xueqi
    Liu, Xinzhi
    Zhong, Shouming
    NEUROCOMPUTING, 2021, 419 : 239 - 250
  • [24] Passivity analysis of memristor-based recurrent neural networks with mixed time-varying delays
    Meng, Zhendong
    Xiang, Zhengrong
    NEUROCOMPUTING, 2015, 165 : 270 - 279
  • [25] Absolute exponential stability analysis of delayed neural networks
    Lu, HT
    PHYSICS LETTERS A, 2005, 336 (2-3) : 133 - 140
  • [26] Synchronization analysis of delayed quaternion-valued memristor-based neural networks by a direct analytical approach
    Guo, Jun
    Shi, Yanchao
    Wang, Shengye
    ELECTRONIC RESEARCH ARCHIVE, 2024, 32 (05): : 3377 - 3395
  • [27] Exponential Stability of Markovian Jumping Memristor-Based Neural Networks via Event-Triggered Impulsive Control Scheme
    Yang, Nijing
    Yu, Yongbin
    Zhong, Shouming
    Wang, Xiangxiang
    Shi, Kaibo
    Cai, Jingye
    IEEE ACCESS, 2020, 8 : 32564 - 32574
  • [28] Extended robust global exponential stability for uncertain switched memristor-based neural networks with time-varying delays
    Li, Xiaoqing
    She, Kun
    Zhong, Shouming
    Shi, Kaibo
    Kang, Wei
    Cheng, Jun
    Yu, Yongbin
    APPLIED MATHEMATICS AND COMPUTATION, 2018, 325 : 271 - 290
  • [29] Passivity and Passification of Memristor-Based Recurrent Neural Networks With Time-Varying Delays
    Guo, Zhenyuan
    Wang, Jun
    Yan, Zheng
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2014, 25 (11) : 2099 - 2109
  • [30] Reliable stabilization for memristor-based recurrent neural networks with time-varying delays
    Mathiyalagan, K.
    Anbuvithya, R.
    Sakthivel, R.
    Park, Ju H.
    Prakash, P.
    NEUROCOMPUTING, 2015, 153 : 140 - 147
12345