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Lookup NU author(s): Dr Jichun Li
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
IEEEA dynamic gain fixed-time (FXT) robust zeroing neural network (DFTRZNN) model is proposed to effectively solve time-variant equality constrained quaternion least squares problem (TV-EQLS). The proposed approach surmounts the shortcomings of conventional numerical algorithms which fail to address time-variant problems. The DFTRZNN model is constructed with a novel dynamic gain parameter and a novel activation function (NAF), which differs from previous zeroing neural network (ZNN) models. Moreover, the comprehensive theoretical derivation of the FXT stability and robustness of the DFTRZNN model is presented in detail. Simulation results further confirm the availability and superiority of the DFTRZNN model for solving TV-EQLS. Finally, the consensus protocols of multiagent systems are presented by utilizing the design scheme of the DFTRZNN model, which further demonstrates its practical application value.
Author(s): Cao P, Xiao L, He Y, Li J
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Networks and Learning Systems
Year: 2023
Pages: epub ahead of print
Online publication date: 06/10/2023
Acceptance date: 10/09/2023
Date deposited: 23/11/2023
ISSN (print): 2162-237X
ISSN (electronic): 2162-2388
Publisher: IEEE
URL: https://doi.org/10.1109/TNNLS.2023.3315332
DOI: 10.1109/TNNLS.2023.3315332
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