Paper Info
Title
Comprehensive Analysis of a New Varying Parameter Zeroing Neural Network for Time Varying Matrix Inversion
Abstract
The matrix inversion problem plays a very important role in mathematics as well as practical engineering applications. In this article, unlike the traditional fixed-parameter zeroing neural network (ZNN) model, on the basis of the original varying-parameter ZNN (VPZNN) model, an improved VPZNN (IVPZNN) model is established and researched to solve time-varying matrix inversion (TVMI). Specifically, the value of the proposed novel time-varying parameter in the IVPZNN model can grow rapidly over time, which can better meet the needs of ZNN in hardware implementation. In addition, theoretical analyses of the novel time varying parameter and the proposed IVPZNN model are given to guarantee the global superexponential convergence and finite-time convergence. Numerical calculation results verify the superior property of the established IVPZNN model for addressing the TVMI problem, as compared with the existing fixed-parameter ZNN and VPZNN models.
Year
DOI
Venue
2021
10.1109/TII.2020.2989173
IEEE Transactions on Industrial Informatics
Keywords
DocType
Volume
Finite-time convergence,superexponential convergence,time-varying matrix inversion (TVMI),varying parameter,zeroing neural network (ZNN)
Journal
17
Issue
ISSN
Citations 
3
1551-3203
0
PageRank 
References 
Authors
0.34
18
5
Name
Order
Citations
PageRank
Lin Xiao156242.84
Yongsheng Zhang220443.58
Jianhua Dai389651.62
Qiuyue Zuo4103.16
Shoujin Wang56513.10