IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i11p2556-d1162933.html
   My bibliography  Save this article

An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization

Author

Listed:
  • Hang Yi

    (School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
    These authors contributed equally to this work.)

  • Wenjun Peng

    (School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China
    These authors contributed equally to this work.)

  • Xiuchun Xiao

    (School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China)

  • Shaojin Feng

    (School of Electronic and Information Engineering, Guangdong Ocean University, Zhanjiang 524088, China)

  • Hengde Zhu

    (School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK)

  • Yudong Zhang

    (School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK)

Abstract

The field of position tracking control and communication engineering has been increasingly interested in time-varying quadratic minimization (TVQM). While traditional zeroing neural network (ZNN) models have been effective in solving TVQM problems, they have limitations in adapting their convergence rate to the commonly used convex activation function. To address this issue, we propose an adaptive non-convex activation zeroing neural network (AZNNNA) model in this paper. Using the Lyapunov theory, we theoretically analyze the global convergence and noise-immune characteristics of the proposed AZNNNA model under both noise-free and noise-perturbed scenarios. We also provide computer simulations to illustrate the effectiveness and superiority of the proposed model. Compared to existing ZNN models, our proposed AZNNNA model outperforms them in terms of efficiency, accuracy, and robustness. This has been demonstrated in the simulation experiment of this article.

Suggested Citation

  • Hang Yi & Wenjun Peng & Xiuchun Xiao & Shaojin Feng & Hengde Zhu & Yudong Zhang, 2023. "An Adaptive Zeroing Neural Network with Non-Convex Activation for Time-Varying Quadratic Minimization," Mathematics, MDPI, vol. 11(11), pages 1-15, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2556-:d:1162933
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/11/2556/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/11/2556/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wang, Guancheng & Li, Qinrou & Liu, Shaoqing & Xiao, Hua & Zhang, Bob, 2022. "New zeroing neural network with finite-time convergence for dynamic complex-value linear equation and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 164(C).
    2. Killian, M. & Zauner, M. & Kozek, M., 2018. "Comprehensive smart home energy management system using mixed-integer quadratic-programming," Applied Energy, Elsevier, vol. 222(C), pages 662-672.
    Full references (including those not matched with items on IDEAS)

    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. Roberto Casado-Vara & Angel Martín del Rey & Ricardo S. Alonso & Saber Trabelsi & Juan M. Corchado, 2020. "A New Stability Criterion for IoT Systems in Smart Buildings: Temperature Case Study," Mathematics, MDPI, vol. 8(9), pages 1-13, August.
    2. Esmeralda López-Garza & René Fernando Domínguez-Cruz & Fernando Martell-Chávez & Iván Salgado-Tránsito, 2022. "Fuzzy Logic and Linear Programming-Based Power Grid-Enhanced Economical Dispatch for Sustainable and Stable Grid Operation in Eastern Mexico," Energies, MDPI, vol. 15(11), pages 1-18, June.
    3. Liu, Yinyan & Ma, Jin & Xing, Xinjie & Liu, Xinglu & Wang, Wei, 2022. "A home energy management system incorporating data-driven uncertainty-aware user preference," Applied Energy, Elsevier, vol. 326(C).
    4. Zhao, Liyuan & Yang, Ting & Li, Wei & Zomaya, Albert Y., 2022. "Deep reinforcement learning-based joint load scheduling for household multi-energy system," Applied Energy, Elsevier, vol. 324(C).
    5. Mak, Davye & Choi, Dae-Hyun, 2020. "Optimization framework for coordinated operation of home energy management system and Volt-VAR optimization in unbalanced active distribution networks considering uncertainties," Applied Energy, Elsevier, vol. 276(C).
    6. Joaquín Garrido-Zafra & Antonio Moreno-Munoz & Aurora Gil-de-Castro & Emilio J. Palacios-Garcia & Carlos D. Moreno-Moreno & Tomás Morales-Leal, 2019. "A Novel Direct Load Control Testbed for Smart Appliances," Energies, MDPI, vol. 12(17), pages 1-16, August.
    7. El-Baz, Wessam & Tzscheutschler, Peter & Wagner, Ulrich, 2019. "Integration of energy markets in microgrids: A double-sided auction with device-oriented bidding strategies," Applied Energy, Elsevier, vol. 241(C), pages 625-639.
    8. Ebrahimi, Seyyed Reza & Rahimiyan, Morteza & Assili, Mohsen & Hajizadeh, Amin, 2022. "Home energy management under correlated uncertainties: A statistical analysis through Copula," Applied Energy, Elsevier, vol. 305(C).
    9. Bartolucci, Lorenzo & Cordiner, Stefano & Mulone, Vincenzo & Santarelli, Marina, 2019. "Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance," Energy, Elsevier, vol. 172(C), pages 997-1004.
    10. Aoun, Nadine & Bavière, Roland & Vallée, Mathieu & Aurousseau, Antoine & Sandou, Guillaume, 2019. "Modelling and flexible predictive control of buildings space-heating demand in district heating systems," Energy, Elsevier, vol. 188(C).
    11. Fang, Qi & Wang, Mingzhu & Li, Xiaodi, 2023. "Event-triggered distributed delayed impulsive control for nonlinear systems with applications to complex networks," Chaos, Solitons & Fractals, Elsevier, vol. 175(P1).
    12. Ma, Yiju & Azuatalam, Donald & Power, Thomas & Chapman, Archie C. & Verbič, Gregor, 2019. "A novel probabilistic framework to study the impact of photovoltaic-battery systems on low-voltage distribution networks," Applied Energy, Elsevier, vol. 254(C).
    13. Bharath Varsh Rao & Friederich Kupzog & Martin Kozek, 2018. "Phase Balancing Home Energy Management System Using Model Predictive Control," Energies, MDPI, vol. 11(12), pages 1-19, November.
    14. Bürger, Adrian & Bohlayer, Markus & Hoffmann, Sarah & Altmann-Dieses, Angelika & Braun, Marco & Diehl, Moritz, 2020. "A whole-year simulation study on nonlinear mixed-integer model predictive control for a thermal energy supply system with multi-use components," Applied Energy, Elsevier, vol. 258(C).
    15. Zhou, Chenghan & Jia, Hongjie & Jin, Xiaolong & Mu, Yunfei & Yu, Xiaodan & Xu, Xiandong & Li, Binghui & Sun, Weichen, 2023. "Two-stage robust optimization for space heating loads of buildings in integrated community energy systems," Applied Energy, Elsevier, vol. 331(C).
    16. Isaías Gomes & Karol Bot & Maria Graça Ruano & António Ruano, 2022. "Recent Techniques Used in Home Energy Management Systems: A Review," Energies, MDPI, vol. 15(8), pages 1-41, April.
    17. Choi, Dong Gu & Murali, Karthik, 2022. "The impact of heterogeneity in consumer characteristics on the design of optimal time-of-use tariffs," Energy, Elsevier, vol. 254(PB).
    18. Zheng, Boyu & Han, Zhiyong & Li, Chunquan & Zhang, Zhijun & Yu, Junzhi & Liu, Peter X., 2024. "A flexible-predefined-time convergence and noise-suppression ZNN for solving time-variant Sylvester equation and its application to robotic arm," Chaos, Solitons & Fractals, Elsevier, vol. 178(C).
    19. Zhang, Wuxia & Wu, Yupeng & Calautit, John Kaiser, 2022. "A review on occupancy prediction through machine learning for enhancing energy efficiency, air quality and thermal comfort in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    20. Fleiß, Eva & Hatzl, Stefanie & Rauscher, Jürgen, 2024. "Smart energy technology: A survey of adoption by individuals and the enabling potential of the technologies," Technology in Society, Elsevier, vol. 76(C).

    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:gam:jmathe:v:11:y:2023:i:11:p:2556-:d:1162933. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.