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New zeroing neural network with finite-time convergence for dynamic complex-value linear equation and its applications

Author

Listed:
  • Wang, Guancheng
  • Li, Qinrou
  • Liu, Shaoqing
  • Xiao, Hua
  • Zhang, Bob

Abstract

This paper proposes a new zeroing neural network (NZNN) for solving the dynamic complex value linear equation (DCVLE). To achieve a faster convergence rate and improve the feasibility of the ZNN model, a bounded nonlinear mapping function is designed that endowed the NZNN model with finite-time convergence. Furthermore, regarding the different forms of the DCVLE in the Cartesian complex plane and the polar complex plane, two distinct NZNN models are proposed. In addition, the global convergence and the finite-time convergence of the NZNN models are analyzed and demonstrated by numerical simulations. Lastly, the NZNN model is successfully applied to the acoustic location and the control of a robotic manipulator, which well demonstrates its feasibility and efficiency.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:164:y:2022:i:c:s0960077922008530
    DOI: 10.1016/j.chaos.2022.112674
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    References listed on IDEAS

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    1. Suo, Jian & Dong, Haitao & Shen, Xiaohong & Wang, Haiyan, 2020. "Bistable stochastic resonance with linear amplitude response enhanced vector DOA estimation under low SNR conditions," Chaos, Solitons & Fractals, Elsevier, vol. 136(C).
    2. Kaboudian, Abouzar & Cherry, Elizabeth M. & Fenton, Flavio H., 2019. "Large-scale interactive numerical experiments of chaos, solitons and fractals in real time via GPU in a web browser," Chaos, Solitons & Fractals, Elsevier, vol. 121(C), pages 6-29.
    3. Krysko-jr, V.A. & Awrejcewicz, J. & Krylova, E.Yu. & Papkova, I.V., 2022. "Mathematical modeling of nonlinear thermodynamics of nanoplates," Chaos, Solitons & Fractals, Elsevier, vol. 158(C).
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    Citations

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    Cited by:

    1. 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.
    2. 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).
    3. Fu, Dongyang & Si, Yang & Wang, Difeng & Xiong, Yizhen, 2024. "An accelerated neural dynamics model for solving dynamic nonlinear optimization problem and its applications," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    4. Miao, Peng & Zheng, Yuhua & Li, Shuai, 2024. "A new FXTZNN model for solving TVCS equation and application to pseudo-inverse of a matrix," Applied Mathematics and Computation, Elsevier, vol. 465(C).
    5. 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).

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