Mittag-Leffler stability and application of delayed fractional-order competitive neural networks

被引：2
|
作者：
Zhang, Fanghai [1 ]
Huang, Tingwen [2 ]
Wu, Ailong [3 ]
Zeng, Zhigang [4 ]
机构：
[1] School of Electrical Engineering and Automation, Hefei University of Technology, Hefei,230009, China
[2] Department of Science Program, Texas A&M University at Qatar, Doha, Qatar
[3] College of Mathematics and Statistics, Hubei Normal University, Huangshi,435002, China
[4] School of Automation, Huazhong University of Science and Technology, Wuhan,430074, China
来源：
Neural Networks | 2024年 / 179卷
基金：
中国国家自然科学基金;
关键词：
Image enhancement;
D O I：
10.1016/j.neunet.2024.106501
中图分类号：
学科分类号：
摘要：
In the article, the Mittag-Leffler stability and application of delayed fractional-order competitive neural networks (FOCNNs) are developed. By virtue of the operator pair, the conditions of the coexistence of equilibrium points (EPs) are discussed and analyzed for delayed FOCNNs, in which the derived conditions of coexistence improve the existing results. In particular, these conditions are simplified in FOCNNs with stepped activations. Furthermore, the Mittag-Leffler stability of delayed FOCNNs is established by using the principle of comparison, which enriches the methodologies of fractional-order neural networks. The results on the obtained stability can be used to design the horizontal line detection of images, which improves the practicability of image detection results. Two simulations are displayed to validate the superiority of the obtained results. © 2024 Elsevier Ltd
引用
收藏
相关论文
共 50 条
  • [1] Mittag-Leffler stability of fractional-order Hopfield neural networks
    Zhang, Shuo
    Yu, Yongguang
    Wang, Hu
    [J]. NONLINEAR ANALYSIS-HYBRID SYSTEMS, 2015, 16 : 104 - 121
  • [2] Mittag-Leffler stability and generalized Mittag-Leffler stability of fractional-order gene regulatory networks
    Ren, Fengli
    Cao, Feng
    Cao, Jinde
    [J]. NEUROCOMPUTING, 2015, 160 : 185 - 190
  • [3] Multiple Mittag-Leffler Stability of Fractional-Order Recurrent Neural Networks
    Liu, Peng
    Zeng, Zhigang
    Wang, Jun
    [J]. IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS, 2017, 47 (08): : 2279 - 2288
  • [4] Multiple Mittag-Leffler stability of fractional-order competitive neural networks with Gaussian activation functions
    Liu, Pingping
    Nie, Xiaobing
    Liang, Jinling
    Cao, Jinde
    [J]. NEURAL NETWORKS, 2018, 108 : 452 - 465
  • [5] Global Mittag-Leffler synchronization of delayed fractional-order memristive neural networks
    Yingjie Fan
    Xia Huang
    Zhen Wang
    Jianwei Xia
    Yuxia Li
    [J]. Advances in Difference Equations, 2018
  • [6] Global Mittag-Leffler synchronization of delayed fractional-order memristive neural networks
    Fan, Yingjie
    Huang, Xia
    Wang, Zhen
    Xia, Jianwei
    Li, Yuxia
    [J]. ADVANCES IN DIFFERENCE EQUATIONS, 2018,
  • [7] Multiple Mittag-Leffler Stability of Almost Periodic Solutions for Fractional-Order Delayed Neural Networks: Distributed
    Song, Chenxi
    Qin, Sitian
    Zeng, Zhigang
    [J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2023, : 1 - 13
  • [8] Multiple Mittag-Leffler stability and locally asymptotical ω-periodicity for fractional-order neural networks
    Wan, Liguang
    Wu, Ailong
    [J]. NEUROCOMPUTING, 2018, 315 : 272 - 282
  • [9] LMI conditions to global Mittag-Leffler stability of fractional-order neural networks with impulses
    Wu, Huaiqin
    Zhang, Xinxin
    Xue, Shunhui
    Wang, Lifei
    Wang, Yu
    [J]. NEUROCOMPUTING, 2016, 193 : 148 - 154
  • [10] Boundedness, Mittag-Leffler stability and asymptotical ω-periodicity of fractional-order fuzzy neural networks
    Wu, Ailong
    Zeng, Zhigang
    [J]. NEURAL NETWORKS, 2016, 74 : 73 - 84
12345