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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
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    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.
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