IDEAS home Printed from https://ideas.repec.org/a/eee/apmaco/v399y2021ics0096300321000837.html
   My bibliography  Save this article

New study on fixed-time synchronization control of delayed inertial memristive neural networks

Author

Listed:
  • Dong, Shiyu
  • Zhu, Hong
  • Zhong, Shouming
  • Shi, Kaibo
  • Liu, Yajuan

Abstract

The problem of fixed-time synchronization (FTS) control for memristive neural networks (MNNs) with discrete timedelay and inertia term is investigated in this work. Firstly, delayed inertial memristive neural networks (DIMNNs) are characterized as the differential equations, which is second-order and has discontinuous right-hand side. Secondly, a suitable two-parameter variable substitution method is advocated for the second-order derivatives of states, and then DIMNNs can be represented in the shape of first-order differential equation. Thirdly, a new fixed-time criterion and FTS of DIMNNs are achieved under the designed feedback controller in the light of comparison lemma and inequality techniques. Meanwhile, the settling-time (ST) of FTS is estimated, it could be adjusted to arbitrary expected values by controller parameters without relying on the initial conditions and also fully reflects that a tighter ST value is obtained. Finally, the effectiveness of the brought forward theoretical results is testified via a numerical simulation.

Suggested Citation

  • Dong, Shiyu & Zhu, Hong & Zhong, Shouming & Shi, Kaibo & Liu, Yajuan, 2021. "New study on fixed-time synchronization control of delayed inertial memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 399(C).
    • Handle: RePEc:eee:apmaco:v:399:y:2021:i:c:s0096300321000837
    DOI: 10.1016/j.amc.2021.126035
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0096300321000837
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.amc.2021.126035?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Chao Du & Fuxi Cai & Mohammed A. Zidan & Wen Ma & Seung Hwan Lee & Wei D. Lu, 2017. "Reservoir computing using dynamic memristors for temporal information processing," Nature Communications, Nature, vol. 8(1), pages 1-10, December.
    2. Xiao, Jianying & Zhong, Shouming, 2018. "Extended dissipative conditions for memristive neural networks with multiple time delays," Applied Mathematics and Computation, Elsevier, vol. 323(C), pages 145-163.
    3. Dmitri B. Strukov & Gregory S. Snider & Duncan R. Stewart & R. Stanley Williams, 2008. "The missing memristor found," Nature, Nature, vol. 453(7191), pages 80-83, May.
    4. Sakthivel, R. & Anbuvithya, R. & Mathiyalagan, K. & Ma, Yong-Ki & Prakash, P., 2016. "Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 213-228.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Fan, Yi & Wang, Pengjun & Heidari, Ali Asghar & Chen, Huiling & HamzaTurabieh, & Mafarja, Majdi, 2022. "Random reselection particle swarm optimization for optimal design of solar photovoltaic modules," Energy, Elsevier, vol. 239(PA).
    2. Jiang, Tingting & Zhang, Yuping & Zeng, Yong & Zhong, Shouming & Shi, Kaibo & Cai, Xiao, 2021. "Finite-time analysis for networked predictive control systems with induced time delays and data packet dropouts," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 581(C).
    3. Alsaedi, Ahmed & Cao, Jinde & Ahmad, Bashir & Alshehri, Ahmed & Tan, Xuegang, 2022. "Synchronization of master-slave memristive neural networks via fuzzy output-based adaptive strategy," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
    4. Liu, Yun & Heidari, Ali Asghar & Ye, Xiaojia & Liang, Guoxi & Chen, Huiling & He, Caitou, 2021. "Boosting slime mould algorithm for parameter identification of photovoltaic models," Energy, Elsevier, vol. 234(C).
    5. Gao, Shang & Peng, Keyu & Zhang, Chunrui, 2021. "Existence and global exponential stability of periodic solutions for feedback control complex dynamical networks with time-varying delays," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Rohit Abraham John & Yiğit Demirağ & Yevhen Shynkarenko & Yuliia Berezovska & Natacha Ohannessian & Melika Payvand & Peng Zeng & Maryna I. Bodnarchuk & Frank Krumeich & Gökhan Kara & Ivan Shorubalko &, 2022. "Reconfigurable halide perovskite nanocrystal memristors for neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    2. Qin, Xiaoli & Wang, Cong & Li, Lixiang & Peng, Haipeng & Yang, Yixian & Ye, Lu, 2018. "Finite-time modified projective synchronization of memristor-based neural network with multi-links and leakage delay," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 302-315.
    3. Chang, Wenting & Zhu, Song & Li, Jinyu & Sun, Kaili, 2018. "Global Mittag–Leffler stabilization of fractional-order complex-valued memristive neural networks," Applied Mathematics and Computation, Elsevier, vol. 338(C), pages 346-362.
    4. Zhongfang Zhang & Xiaolong Zhao & Xumeng Zhang & Xiaohu Hou & Xiaolan Ma & Shuangzhu Tang & Ying Zhang & Guangwei Xu & Qi Liu & Shibing Long, 2022. "In-sensor reservoir computing system for latent fingerprint recognition with deep ultraviolet photo-synapses and memristor array," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Jaehyun Kang & Taeyoon Kim & Suman Hu & Jaewook Kim & Joon Young Kwak & Jongkil Park & Jong Keuk Park & Inho Kim & Suyoun Lee & Sangbum Kim & YeonJoo Jeong, 2022. "Cluster-type analogue memristor by engineering redox dynamics for high-performance neuromorphic computing," Nature Communications, Nature, vol. 13(1), pages 1-10, December.
    6. Zhang, Ruimei & Zeng, Deqiang & Zhong, Shouming & Yu, Yongbin, 2017. "Event-triggered sampling control for stability and stabilization of memristive neural networks with communication delays," Applied Mathematics and Computation, Elsevier, vol. 310(C), pages 57-74.
    7. Min, Fuhong & Zhang, Wen & Ji, Ziyi & Zhang, Lei, 2021. "Switching dynamics of a non-autonomous FitzHugh-Nagumo circuit with piecewise-linear flux-controlled memristor," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    8. Feng, Liang & Hu, Cheng & Yu, Juan & Jiang, Haijun & Wen, Shiping, 2021. "Fixed-time Synchronization of Coupled Memristive Complex-valued Neural Networks," Chaos, Solitons & Fractals, Elsevier, vol. 148(C).
    9. Hu, Yongbing & Li, Qian & Ding, Dawei & Jiang, Li & Yang, Zongli & Zhang, Hongwei & Zhang, Zhixin, 2021. "Multiple coexisting analysis of a fractional-order coupled memristive system and its application in image encryption," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
    10. Yan, Dengwei & Wang, Lidan & Duan, Shukai & Chen, Jiaojiao & Chen, Jiahao, 2021. "Chaotic Attractors Generated by a Memristor-Based Chaotic System and Julia Fractal," Chaos, Solitons & Fractals, Elsevier, vol. 146(C).
    11. Luo, Mengzhuo & Cheng, Jun & Liu, Xinzhi & Zhong, Shouming, 2019. "An extended synchronization analysis for memristor-based coupled neural networks via aperiodically intermittent control," Applied Mathematics and Computation, Elsevier, vol. 344, pages 163-182.
    12. Liu, Shuxin & Yu, Yongguang & Zhang, Shuo & Zhang, Yuting, 2018. "Robust stability of fractional-order memristor-based Hopfield neural networks with parameter disturbances," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 509(C), pages 845-854.
    13. Zhang, Ge & Ma, Jun & Alsaedi, Ahmed & Ahmad, Bashir & Alzahrani, Faris, 2018. "Dynamical behavior and application in Josephson Junction coupled by memristor," Applied Mathematics and Computation, Elsevier, vol. 321(C), pages 290-299.
    14. Chen, Qun & Li, Bo & Yin, Wei & Jiang, Xiaowei & Chen, Xiangyong, 2023. "Bifurcation, chaos and fixed-time synchronization of memristor cellular neural networks," Chaos, Solitons & Fractals, Elsevier, vol. 171(C).
    15. Stavrinides, Stavros G. & Hanias, Michael P. & Gonzalez, Mireia B. & Campabadal, Francesca & Contoyiannis, Yiannis & Potirakis, Stelios M. & Al Chawa, Mohamad Moner & de Benito, Carol & Tetzlaff, Rona, 2022. "On the chaotic nature of random telegraph noise in unipolar RRAM memristor devices," Chaos, Solitons & Fractals, Elsevier, vol. 160(C).
    16. Zhiwei Chen & Wenjie Li & Zhen Fan & Shuai Dong & Yihong Chen & Minghui Qin & Min Zeng & Xubing Lu & Guofu Zhou & Xingsen Gao & Jun-Ming Liu, 2023. "All-ferroelectric implementation of reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-12, December.
    17. Li, Liangchen & Xu, Rui & Lin, Jiazhe, 2020. "Lagrange stability for uncertain memristive neural networks with Lévy noise and leakage delay," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 549(C).
    18. Sakthivel, R. & Anbuvithya, R. & Mathiyalagan, K. & Ma, Yong-Ki & Prakash, P., 2016. "Reliable anti-synchronization conditions for BAM memristive neural networks with different memductance functions," Applied Mathematics and Computation, Elsevier, vol. 275(C), pages 213-228.
    19. Xiangpeng Liang & Yanan Zhong & Jianshi Tang & Zhengwu Liu & Peng Yao & Keyang Sun & Qingtian Zhang & Bin Gao & Hadi Heidari & He Qian & Huaqiang Wu, 2022. "Rotating neurons for all-analog implementation of cyclic reservoir computing," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    20. Hongwei Tan & Sebastiaan van Dijken, 2023. "Dynamic machine vision with retinomorphic photomemristor-reservoir computing," Nature Communications, Nature, vol. 14(1), pages 1-9, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:apmaco:v:399:y:2021:i:c:s0096300321000837. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: https://www.journals.elsevier.com/applied-mathematics-and-computation .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.