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A noise tolerant parameter-variable zeroing neural network and its applications

Author

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  • Jin, Jie
  • Chen, Weijie
  • Qiu, Lixin
  • Zhu, Jingcan
  • Liu, Haiyan

Abstract

Time-varying problems frequently arise in the territories of science and engineering, and most of the time-varying problems can be described by dynamic matrix equations. As a powerful tool for solving dynamic matrix equations, the zeroing neural network (ZNN) develops fast in recent years. Convergence and robustness are two main performance indicators of the ZNN model. However, the development of the ZNN is focused on the improvement of its convergence in the past, and its robustness to noises is rarely considered. In order to achieve fast convergence and robustness of the ZNN model, a novel activation function (NAF) is presented in this paper. Based on the NAF, a noise-tolerant parameter-variable ZNN (NTPVZNN) model for solving dynamic Sylvester matrix equations (DSME) is realized, and its fixed-time convergence and robustness to noises are verified by rigorous mathematical analysis and numerical simulation results. Besides, two examples of electrical circuit currents computing and robotic manipulator trajectory tracking using the proposed NTPVZNN model in noisy environment further demonstrates its practical application ability.

Suggested Citation

  • Jin, Jie & Chen, Weijie & Qiu, Lixin & Zhu, Jingcan & Liu, Haiyan, 2023. "A noise tolerant parameter-variable zeroing neural network and its applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 207(C), pages 482-498.
    • Handle: RePEc:eee:matcom:v:207:y:2023:i:c:p:482-498
    DOI: 10.1016/j.matcom.2023.01.012
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    References listed on IDEAS

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    1. Sobehart, Lionel & Harada, Hiroyuki, 2018. "High performance rigid body simulation of modularized robots using constraint-based models," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 144(C), pages 91-107.
    2. Zhao, Lv & Jin, Jie & Gong, Jianqiang, 2021. "Robust zeroing neural network for fixed-time kinematic control of wheeled mobile robot in noise-polluted environment," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 289-307.
    3. Xiao, Lin & Yi, Qian & Zuo, Qiuyue & He, Yongjun, 2020. "Improved finite-time zeroing neural networks for time-varying complex Sylvester equation solving," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 178(C), pages 246-258.
    4. My, Chu Anh & Makhanov, Stanislav S. & Van, Nguyen A. & Duc, Vu M., 2020. "Modeling and computation of real-time applied torques and non-holonomic constraint forces/moment, and optimal design of wheels for an autonomous security robot tracking a moving target," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 170(C), pages 300-315.
    5. Stanimirović, Predrag & Gerontitis, Dimitris & Tzekis, Panagiotis & Behera, Ratikanta & Sahoo, Jajati Keshari, 2021. "Simulation of Varying Parameter Recurrent Neural Network with application to matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 185(C), pages 614-628.
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    Cited by:

    1. 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).

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