Search Advanced
2024 Volume 33 Issue 12
Article Contents
Previous Article Next Article

Qing-Qing Ma(马青青), An-Jiang Lu(陆安江)†, and Zhi Huang(黄智). 2024: Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor, Chinese Physics B, 33(12): 120502. doi: 10.1088/1674-1056/ad8148
Citation: Qing-Qing Ma(马青青), An-Jiang Lu(陆安江)†, and Zhi Huang(黄智). 2024: Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor, Chinese Physics B, 33(12): 120502. doi: 10.1088/1674-1056/ad8148
shu

Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor

  • College of Big Data and Information Engineering, Guizhou University, Guiyang 550025, China

  • Received Date: 13/08/2024
    Accepted Date: 24/09/2024
  • Fund Project:

    This paper was supported by the Guizhou Province Natural Science Foundation (Qiankehe Fundamentals-ZK[2023]General-055) and Guizhou Province Science and Technology Support Plan Project (Qiankehe Fundamentals [2023] General-465).

  • A method of generating multi-double scroll attractors is proposed based on the memristor Hopfield neural network (HNN) under pulse control. First, the original hyperbolic-type memristor is added to the neural network mathematical model, and the influence of this memristor on the dynamic behavior of the new HNN is analyzed. The numerical results show that after adding the memristor, the abundant dynamic behaviors such as chaos coexistence, period coexistence and chaos period coexistence can be observed when the initial value of the system is changed. Then the logic pulse is added to the external memristor. It is found that the equilibrium point of the HNN can multiply and generate multi-double scroll attractors after the pulse stimulation. When the number of logical pulses is changed, the number of multi-double scroll attractors will also change, so that the pulse can control the generation of multi-double scroll attractors. Finally, the HNN circuit under pulsed stimulation was realized by circuit simulation, and the results verified the correctness of the numerical results.
  • 加载中
  • Vahid K, Fereidoun R N and Ali S 2022 Biomedical Signal Processing and Control 78 103852

    Google Scholar Pub Med

    Wang H, Lu Q and Wang Q 2008 Communications in Nonlinear Science and Numerical Simulation 13 1668

    Google Scholar Pub Med

    Zhou Z W and Wang S 2022 Physica Scripta 97 025206

    Google Scholar Pub Med

    Ichinose N 2021 International Journal of Bifurcation and Chaos 31 2130003

    Google Scholar Pub Med

    Yu F, Chen H and Kong X 2024 Euro. Phys. J. Plus 137 434

    Google Scholar Pub Med

    Yu F, Wu C, Lin Y, et al. 2024 Nonlinear Dyn. 112 12393

    Google Scholar Pub Med

    Lai Q, Lai C, Kamdem D P K, Li C and He S 2022 International Journal of Bifurcation and Chaos 32 2250042

    Google Scholar Pub Med

    Yang F and Wang X 2021 Physica Scripta 96 035218

    Google Scholar Pub Med

    Castellanos-Jaramillo J, Castellanos-Moreno A and Corella-Madueño A 2020 Physica Scripta 95 075002

    Google Scholar Pub Med

    Fang T, Zhang J, Huang S, Xu F, Wang M and Yang H 2019 Nonlinear Dyn. 98 1267

    Google Scholar Pub Med

    Chen C, Bao H, Chen M, Xu Q and Bao B 2019 AEU-International Journal of Electronics and Communication. 111 152894

    Google Scholar Pub Med

    Yu F, Kong X, Yao W, et al. 2024 Chaos, Solitons & Fractals 179 114440

    Google Scholar Pub Med

    Chen C, Min F, Zhang Y and Bao B 2021 Nonlinear Dyn. 106 2559

    Google Scholar Pub Med

    Bao B, Qian H and Xu Q 2017 Frontiers in Computational Neuroscience 11 81

    Google Scholar Pub Med

    Lai Q and Chen Z 2023 Chaos, Solitons & Fractals 170 113341

    Google Scholar Pub Med

    Lin H, Wang C, Hong Q and Sun Y 2020 IEEE Transactions on Circuits and Systems II: Express Briefs 67 3472

    Google Scholar Pub Med

    Bao H, Hu A, Liu W 2019 International Journal of Bifurcation and Chaos 29 1950006

    Google Scholar Pub Med

    Ge M, Jia Y, Xu Y and Yang L 2018 Nonlinear Dyn. 91 515

    Google Scholar Pub Med

    Ma M, Xie X, Yang Y, Li Z and Sun Y 2023 Chin. Phys. B 32 058701

    Google Scholar Pub Med

    Li J, Liu S, Liu W, Yu Y and Wu Y 2016 Nonlinear Dyn. 83 801

    Google Scholar Pub Med

    Wachtel H, Seaman R and Joines W 1975 Annals of the New York Academy of Sciences 247 46

    Google Scholar Pub Med

    Gianní M, Liberti M and Apollonio F 2006 Biol. Cybern. 94 118

    Google Scholar Pub Med

    Haan W D, Flier W M V D, Koene T, Smits L L, Scheltens P and C J 2012 NeuroImage 59 3085

    Google Scholar Pub Med

    Lin H, Wang C, Yao W and Tan Y 2020 Communications in Nonlinear Science and Numerical Simulation. 90 105390

    Google Scholar Pub Med

    Zhang S, Zheng J H and Wang X 2021 Chaos 31 011101

    Google Scholar Pub Med

    Rech P C 2011 Neurocomputing 74 3361

    Google Scholar Pub Med

    Chen P, Chen Z and Wu W 2010 Chin. Phys. B 19 040509

    Google Scholar Pub Med

    Han S, Kommuri S, Kwon O and Lee S 2022 Applied Mathematics and Computation 423 126994

    Google Scholar Pub Med

    Pham V T, Jafari S, Vaidyanathan S, Volos C andWang X 2016 Science China Technological Sciences 59 358

    Google Scholar Pub Med

    Zheng P, Tang W and Zhang J 2010 Neurocomputing (Amsterdam) 73 2280

    Google Scholar Pub Med

    Yu F, Kong X X, Mokbel A A M, Yao W and Cai S 2023 IEEE Transactions on Circuits and Systems II: Express Briefs 70 326

    Google Scholar Pub Med

    Qin M and Lai Q 2024 Applied Mathematical Modelling 125 125

    Google Scholar Pub Med

    Wu F, Kang T, Shao Y and Wang Q 2023 Chaos, Solitons & Fractals 172 113569

    Google Scholar Pub Med

    Etémé A, Tabi C and Mohamadou 2019 Applied Mathematical Modelling 125 432

    Google Scholar Pub Med

    Sun J, Li C, Wang Z, et al. 2024 IEEE Transactions on Industrial Informatics 20 3778

    Google Scholar Pub Med

    Bao H, Chen Z G, Cai J M, et al. 2022 Science China Technological Sciences 65 2582

    Google Scholar Pub Med

    Lin H, Wang C, Cui L, et al. 2022 Nonlinear Dyn. 110 841

    Google Scholar Pub Med

  • 加载中
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
Export Citation
PDF
XML

Article Metrics

Article views(313) PDF downloads(1) Cited by(0)

Access History

Coexisting and multiple scroll attractors in a Hopfield neural network with a controlled memristor

  • Received Date: 13/08/2024
  • Accepted Date: 24/09/2024
Fund Project: 

Abstract: A method of generating multi-double scroll attractors is proposed based on the memristor Hopfield neural network (HNN) under pulse control. First, the original hyperbolic-type memristor is added to the neural network mathematical model, and the influence of this memristor on the dynamic behavior of the new HNN is analyzed. The numerical results show that after adding the memristor, the abundant dynamic behaviors such as chaos coexistence, period coexistence and chaos period coexistence can be observed when the initial value of the system is changed. Then the logic pulse is added to the external memristor. It is found that the equilibrium point of the HNN can multiply and generate multi-double scroll attractors after the pulse stimulation. When the number of logical pulses is changed, the number of multi-double scroll attractors will also change, so that the pulse can control the generation of multi-double scroll attractors. Finally, the HNN circuit under pulsed stimulation was realized by circuit simulation, and the results verified the correctness of the numerical results.

    HTML

Reference (37)

Catalog

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return