A Novel High-Efficiency Variable Parameter Double Integration ZNN Model for Time-Varying Sylvester Equations

In this paper, a High-Efficiency Variable Parameter Double Integration Zeroing Neural Network (HEVPDIZNN) model combining variable parameter function and double integration is proposed to solve the time-varying Sylvester matrix equations, using the decreasing function with a large initial value as the variable parameter. This design achieves faster convergence and higher accuracy after stabilization.The use of double integral terms ensures that the model has higher solution accuracy and effectively suppresses constant noise, linear noise, and quadratic noise. The article proves the convergence and robustness of the model through theoretical analysis. In the comparison experiments with the existing models (MNTZNN, NTPVZNN, NSVPZNN, NSRNN, and ADIZNN), it is confirmed that HEVPDIZNN has faster convergence speed, the average error at the time of stabilization is about ${10}^{-5}$ times that of the existing models, and it has a better suppression of the linear noise, quadratic noise, and constant noise.