Work

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

Year: 2025

Type: article

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.

Source: Mathematics

DOI: 10.3390/math13111801

Authors: Xin Zhou, Bolin Liao

Institution: Jishou University

Language: English

Topic: Neural Networks and Applications

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: National Natural Science Foundation of China