Paper Info
Title
A Noise-Suppression ZNN Model With New Variable Parameter for Dynamic Sylvester Equation
Abstract
In this article, a noise-suppression variable-parameter zeroing neural network (NSVPZNN) is proposed to handle the dynamic Sylvester equation. Differing from the previous zeroing neural networks (ZNNs), a new nonlinear activation function and an especially constructed time-variant parameter are developed to construct the novel NSVPZNN model. Therefore, the NSVPZNN model can achieve faster predefined-time convergence without noise disturbance and have stronger robust performance under multiple noises. Furthermore, the convergence upper bound of the NSVPZNN model is theoretically calculated, and a detailed proof of guaranteeing noise-tolerance performance is given. Numerical simulations verify that the NSVPZNN has better performance than the ZNN, the finite-time convergence ZNN model, the predefined-time convergence ZNN model, and the other variable-parameter ZNN when handling the dynamic Sylvester equation. Finally, the design method of the NSVPZNN is applied to the wheeled manipulator for tracking the butterfly trajectory, which further illustrates the model's reliability.
Year
DOI
Venue
2021
10.1109/TII.2021.3058343
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Dynamic Sylvester equation,noise tolerance,predefined-time convergence,variable parameter,zeroing neural network (ZNN)
Journal
17
Issue
ISSN
Citations 
11
1551-3203
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Lin Xiao19415.07
Yongjun He234.13