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Lookup NU author(s): Dr Jichun Li
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE, 2024.
For re-use rights please refer to the publisher's terms and conditions.
In this article, a novel distributed gradient neural network (DGNN) with predefined-time convergence (PTC) is proposed to solve consensus problems widely existing in multiagent systems (MASs). Compared with previous gradient neural networks (GNNs) for optimization and computation, the proposed DGNN model works in a nonfully connected way, in which each neuron only needs the information of neighbor neurons to converge to the equilibrium point. The convergence and asymptotic stability of the DGNN model are proved according to the Lyapunov theory. In addition, based on a relatively loose condition, three novel nonlinear activation functions are designed to speedup the DGNN model to PTC, which is proved by rigorous theory. Computer numerical results further verify the effectiveness, especially the PTC, of the proposed nonlinearly activated DGNN model to solve various consensus problems of MASs. Finally, a practical case of the directional consensus is presented to show the feasibility of the DGNN model and a corresponding connectivity-testing example is given to verify the influence on the convergence speed.
Author(s): Xiao L, Jia L, Dai J, Cao Y, Li Y, Zhu Q, Li J, Liu M
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Neural Networks and Learning Systems
Year: 2024
Volume: 35
Issue: 3
Pages: 3478-3487
Print publication date: 01/03/2024
Online publication date: 29/07/2022
Acceptance date: 16/07/2022
Date deposited: 09/08/2022
ISSN (print): 2162-2388
ISSN (electronic): 2162-237X
Publisher: IEEE
URL: https://doi.org/10.1109/TNNLS.2022.3193429
DOI: 10.1109/TNNLS.2022.3193429
ePrints DOI: 10.57711/zajy-ns42
PubMed id: 35905068
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