Advances in ZNN Models for Solving Time-Varying Sylvester Equations: Convergence, Robustness, and Applications | Zendy

Xiangxiang Xi | Zendy; Peng Miao | Zendy; Yuheng Ding | Zendy

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Advances in ZNN Models for Solving Time-Varying Sylvester Equations: Convergence, Robustness, and Applications

Author(s) -

Xiangxiang Xi,

Peng Miao,

Yuheng Ding

Publication year - 2025

Publication title -

ieee access

Language(s) - English

Resource type - Magazines

SCImago Journal Rank - 0.587

H-Index - 127

eISSN - 2169-3536

DOI - 10.1109/access.2025.3620136

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

This article provides a systematic review of the specific field of solving time-varying Sylvester equations using zeroing neural network (ZNN), and delves into multi parameter joint sensitivity analysis and design strategies for the first time. The ZNN is known for its strong dynamic performance and computational efficiency, making it suitable for resource-limited devices. Because of these advantages, ZNN has become a popular solution for time-varying problems, especially the time-varying Sylvester equation. This paper reviews recent methods for achieving faster finite-time and fixed-time convergence of ZNN when solving this equation. Key aspects such as convergence speed, parameter selection, robustness, and practical applications are discussed. The enhanced ZNN models offer significant potential for real-time applications, including robotic control and online signal processing, where rapid and precise computation is critical. A comparison with traditional approaches highlights ZNN’s superior performance. Future work may focus on developing new activation functions, reducing sensitivity to parameters, and obtaining tighter convergence time estimates.

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