Paper Info
Title
Design and Analysis of a Noise-Resistant ZNN Model for Settling Time-Variant Linear Matrix Inequality in Predefined-Time
Abstract
Aiming at the efficient online solution of the time-variant linear matrix inequality (LMI) under nonideal conditions (e.g., noise pollution), a predefined-time convergent and integral-enhanced zeroing neural network (PCIE-ZNN) model is built for the first time in this article. Compared with existing zeroing neural network (ZNN) models for settling the time-variant LMI, the PCIE-ZNN model proposed in this article is proved to have better convergence and stronger robustness even in the presence of noise interference through strict mathematical analysis and detailed numerical simulations. Specifically, the stability, predefined-time convergence, and robustness of the PCIE-ZNN model are guaranteed in theory. Then, numerical simulation cases fully compare the results of the proposed PCIE-ZNN model and the existing ZNN models for the time-variant LMI, which demonstrates the correctness of theoretical proof and the superiority of the PCIE-ZNN model in settling the time-variant LMI under various noise pollution. In addition, through comparative experiments of three sets of design parameters, the convergence speed of the PCIE-ZNN model can be further accelerated by selecting proper parameters.
Year
DOI
Venue
2022
10.1109/TII.2021.3135383
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Integral term design,predefined-time convergence,robustness,time-variant linear matrix inequality (LMI),zeroing neural network (ZNN)
Journal
18
Issue
ISSN
Citations 
10
1551-3203
0
PageRank 
References 
Authors
0.34
25
6
Name
Order
Citations
PageRank
Lin Xiao19415.07
Wentong Song200.34
Xiaopeng Li317132.15
Lei Jia400.68
Jiayue Sun500.34
Yaonan Wang61150118.92