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Design and Analysis of a Novel Distributed Gradient Neural Network for Solving Consensus Problems in a Predefined Time

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 Institute of Electrical and Electronics Engineers, 2022.

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.

Publication metadata

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: 2022

Pages: epub ahead of print

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: Institute of Electrical and Electronics Engineers


DOI: 10.1109/TNNLS.2022.3193429

ePrints DOI: 10.57711/zajy-ns42

PubMed id: 35905068


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