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An accelerated neural dynamics model for solving dynamic nonlinear optimization problem and its applications

Author

Listed:
  • Fu, Dongyang
  • Si, Yang
  • Wang, Difeng
  • Xiong, Yizhen

Abstract

Zeroing neural dynamics (ZND) model is a powerful tool for solving dynamic problems. This study presents an accelerated neural dynamics (AND) model by solving a dynamic nonlinear optimization (DNO) problem. Different from the classical activation function (AF), the AND model describes a novel accelerated convergence strategy that designs a nonlinear dynamic variable according to the error paradigm. Additionally, the AND model can be converted into a paradigm-based dynamical mode, which provides a quantification of the convergence time. Notably, the AND model shows outstanding robustness to various perturbations in the computational environment. The superiority of the AND model is further validated by comparing different models. Subsequently, the model’s practicality is shown through the utilization of acoustic-based time difference of arrival (TDOA) localization.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:chsofr:v:180:y:2024:i:c:s0960077924000936
    DOI: 10.1016/j.chaos.2024.114542
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    References listed on IDEAS

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    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. Pisano, Alessandro & Usai, Elio, 2011. "Sliding mode control: A survey with applications in math," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(5), pages 954-979.
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