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Stability of delayed inertial neural networks on time scales: A unified matrix-measure approach

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submitted on 2023-10-09, 10:30 and posted on 2023-10-09, 11:04 authored by Qiang Xiao, Tingwen Huang

This note introduces a unified matrix-measure concept to study the stability of a class of inertial neural networks with bounded time delays on time scales. The novel matrix-measure concept unifies the classic matrix-measure and the generalized matrix-measure concept. One sufficient global exponential stability criterion is obtained based on this key matrix-measure and no Lyapunov function is required. To make the stability performance better, another stability criterion in which more detailed information is involved has been acquired. The theoretical results in this note contain and extend some existing continuous-time and discrete-time works. A numerical example is given to show the validity of the results.

Other Information

Published in: Neural Networks
License: http://creativecommons.org/licenses/by/4.0/
See article on publisher's website: https://dx.doi.org/10.1016/j.neunet.2020.06.020

Funding

Open Access funding provided by the Qatar National Library

History

Language

  • English

Publisher

Elsevier

Publication Year

  • 2020

License statement

This Item is licensed under the Creative Commons Attribution 4.0 International License

Institution affiliated with

  • Hamad Bin Khalifa University
  • College of Science and Engineering - HBKU
  • Texas A&M University at Qatar