Advances in Zeroing Neural Networks: Convergence Optimization and Robustness in Dynamic Systems

Xin Zhou and Bolin Liao ()
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Xin Zhou: College of Computer Science and Engineering, Jishou University, Jishou 416000, China
Bolin Liao: College of Computer Science and Engineering, Jishou University, Jishou 416000, China

Mathematics, 2025, vol. 13, issue 11, 1-25

Abstract: Zeroing Neural Networks (ZNNs), an ODE-based neural dynamics framework, has become a pivotal approach for solving time-varying problems in dynamic systems. This paper systematically reviews recent advances in improving the convergence of ZNN models, focusing on the optimization of fixed parameters, dynamic parameters, and activation functions. Additionally, structural adaptations and fuzzy control strategies have significantly enhanced the robustness and disturbance rejection capabilities of these systems. ZNNs have been successfully applied in robotic control, demonstrating superior accuracy and robustness compared to traditional methods. Future research directions include exploring nonlinear activation functions, multimodal adaptation strategies, and scalability in real-world environments.

Keywords: Zeroing Neural Networks; convergence; activation function; structural adaptations; robustness; robotic control (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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