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A combined power activation function based convergent factor-variable ZNN model for solving dynamic matrix inversion

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  • Zhu, Jingcan
  • Jin, Jie
  • Chen, Weijie
  • Gong, Jianqiang

Abstract

The application of zeroing neural network (ZNN) to solve multifarious time-varying problems, especially the dynamic matrix inversion (DMI), is widely used in recent years. As the core components of ZNN model, the activation function (AF) and convergent factor (CF) always occupy a momentous position in its development. In this paper, a convergent factor-variable ZNN (CFVZNN) model with a novel combined power activation function (CPAF) and a time-varying adjustable CF is proposed for online DMI solution. Unlike other existing conventional ZNN (CZNN) models, the proposed CFVZNN model has the advantages in both fixed-time convergence and anti-noise property, and these superiors of the proposed CFVZNN model are verified by strict mathematical derivation. Besides, several successful examples for solving DMI problems and tracking control of mobile manipulator in noisy environment further validate the practical application prospects of the proposed CFVZNN model.

Suggested Citation

  • Zhu, Jingcan & Jin, Jie & Chen, Weijie & Gong, Jianqiang, 2022. "A combined power activation function based convergent factor-variable ZNN model for solving dynamic matrix inversion," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 197(C), pages 291-307.
    • Handle: RePEc:eee:matcom:v:197:y:2022:i:c:p:291-307
    DOI: 10.1016/j.matcom.2022.02.019
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    References listed on IDEAS

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    1. Guo, Dongsheng & Zhang, Yunong, 2015. "ZNN for solving online time-varying linear matrix–vector inequality via equality conversion," Applied Mathematics and Computation, Elsevier, vol. 259(C), pages 327-338.
    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. 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. Wenrui Yang & Yang Gu & Xia Xie & Chengze Jiang & Zhiyuan Song & Yudong Zhang, 2023. "Bounded Adaptive Function Activated Recurrent Neural Network for Solving the Dynamic QR Factorization," Mathematics, MDPI, vol. 11(10), pages 1-18, May.
    2. 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).

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