Predefined-Time Convergence-Based Noise-Tolerant Zeroing Neural Networks for Solving the Time-Varying Sylvester Equation
YUE Yuanda
MI Ling
CHEN Chuan
Abstract：Sylvester equations are often used in mathematics and control theory,and zeroing neural net-works(ZNN)are very effective at solving such time-varying equations.The convergence of ZNN is stud-ied,and a new activation function is designed based on a predefined time stability theorem,and a new ZNN model for solving time-varying Sylvester equations is obtained.This model is called a predefined Time Convergent zeroized neural network(PTZNN)model.Compared with the previous ZNN model,the proposed model improves the convergence speed to reach the predefined time convergence and further im-proves the noise tolerance.Then the simulation results show that the model is better than the known mod-el in solving the time-varying Sylvester equation.
Keywords：zeroing neural networknoise-tolerant capacitypredefined-time convergenceSylvester equa-tion
Publication Date：2024-08-28
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：10( 33-42 )