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A direct analysis method to global h-stability of positive Cohen-Grossberg neural networks with time-varying delays
  • YI-BO SUN,
  • Biwen Li
YI-BO SUN
Hubei Normal University

Corresponding Author:[email protected]

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Biwen Li
Hubei Normal University
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Abstract

In this paper, global h-stability of nonlinear positive Cohen-Grossberg neural network (PCGNN) system with time-varying delays is studied by means of a direct analysis method. By selecting the appropriate h function and determining its differential expression, global h-stability is converted into two types of known stability, that is Lagrangian exponential stability and global exponential stability. For the sake of improving the accuracy of the stability results, we spare no effort to optimize the fitting effect of the system state trajectory by changing the differential expression of the h function. In addition, two examples are given to verify the feasibility and effectiveness of this method in PCGNN.
26 Jul 2023Submitted to Mathematical Methods in the Applied Sciences
26 Jul 2023Submission Checks Completed
26 Jul 2023Assigned to Editor
02 Aug 2023Review(s) Completed, Editorial Evaluation Pending
25 Sep 2023Reviewer(s) Assigned
04 Feb 20241st Revision Received
09 Feb 2024Submission Checks Completed
09 Feb 2024Assigned to Editor
09 Feb 2024Review(s) Completed, Editorial Evaluation Pending
19 Apr 2024Editorial Decision: Revise Major