The Pure and Applied Mathematics (한국수학교육학회지시리즈B:순수및응용수학)

Korean Society of Mathematical Education (한국수학교육학회)
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STABILITY ON POSITIVE ALMOST PERIODIC HIGH-ORDER HOPFIELD NEURAL NETWORKS

  • Feng Liu (School of Mathematics and Statistics, Changsha University of Science and Technology)
  • Received : 2024.06.16
  • Accepted : 2024.10.08
  • Published : 2024.11.30
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Abstract

This essay explores a class of almost periodic high-order Hopfield neural networks involving time-varying delays. By taking advantage of some novel differential inequality techniques, several assertions are derived to substantiate the positive exponential stability of the addressed neural networks, which refines and extends the corresponding results in some existing references. In particular, a demonstrative experiment is presented to check the effectiveness and validity of the theoretical outcomes.

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Acknowledgement

The author would like to thank the referees for their thorough review with constructive suggestions and valuable comments on the paper. Project supported by the National Natural Science Foundation of China (No. 11971076), and the Postgraduate Scientific Research Innovation Project of Hunan Province (No. CX20240079).

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