Paper Info
Title
New Varying-Parameter ZNN Models With Finite-Time Convergence and Noise Suppression for Time-Varying Matrix Moore–Penrose Inversion
Abstract
This article aims to solve the Moore-Penrose inverse of time-varying full-rank matrices in the presence of various noises in real time. For this purpose, two varying-parameter zeroing neural networks (VPZNNs) are proposed. Specifically, VPZNN-R and VPZNN-L models, which are based on a new design formula, are designed to solve the right and left Moore-Penrose inversion problems of time-varying full-rank matrices, respectively. The two VPZNN models are activated by two novel varying-parameter nonlinear activation functions. Detailed theoretical derivations are presented to show the desired finite-time convergence and outstanding robustness of the proposed VPZNN models under various kinds of noises. In addition, existing neural models, such as the original ZNN (OZNN) and the integration-enhanced ZNN (IEZNN), are compared with the VPZNN models. Simulation observations verify the advantages of the VPZNN models over the OZNN and IEZNN models in terms of convergence and robustness. The potential of the VPZNN models for robotic applications is then illustrated by an example of robot path tracking.
Year
DOI
Venue
2020
10.1109/TNNLS.2019.2934734
IEEE Transactions on Neural Networks and Learning Systems
Keywords
DocType
Volume
Finite-time convergence,Moore–Penrose inverse,noise suppression,robustness,varying-parameter zeroing neural network (VPZNN)
Journal
31
Issue
ISSN
Citations 
8
2162-237X
7
PageRank 
References 
Authors
0.42
28
4
Name
Order
Citations
PageRank
Zhiguo Tan1564.40
Weibing Li2363.48
Lin Xiao39415.07
Yueming Hu481.78