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AIMS Mathematics

2024, Volume 9, Issue 7: 19345-19360. doi: 10.3934/math.2024942
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Research article

Stability analysis of delayed neural networks via compound-parameter -based integral inequality

  • 1.
    Internet of Things Department, Henan Institute of Economics and Trade, Zhengzhou 450000, China
  • 2.
    College of Control Science and Engineering, Zhejiang University, Hangzhou, 310058, China
  • 3.
    School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, Singapore
  • 4.
    College of Automation, Chongqing University, 400044, China
  • Received: 18 March 2024 Revised: 28 May 2024 Accepted: 03 June 2024 Published: 11 June 2024

  • MSC : 37C75, 93C55, 92B20

  • This paper revisits the issue of stability analysis of neural networks subjected to time-varying delays. A novel approach, termed a compound-matrix-based integral inequality (CPBII), which accounts for delay derivatives using two adjustable parameters, is introduced. By appropriately adjusting these parameters, the CPBII efficiently incorporates coupling information along with delay derivatives within integral inequalities. By using CPBII, a novel stability criterion is established for neural networks with time-varying delays. The effectiveness of this approach is demonstrated through a numerical illustration.

    Citation: Wenlong Xue, Zhenghong Jin, Yufeng Tian. Stability analysis of delayed neural networks via compound-parameter -based integral inequality[J]. AIMS Mathematics, 2024, 9(7): 19345-19360. doi: 10.3934/math.2024942

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  • This paper revisits the issue of stability analysis of neural networks subjected to time-varying delays. A novel approach, termed a compound-matrix-based integral inequality (CPBII), which accounts for delay derivatives using two adjustable parameters, is introduced. By appropriately adjusting these parameters, the CPBII efficiently incorporates coupling information along with delay derivatives within integral inequalities. By using CPBII, a novel stability criterion is established for neural networks with time-varying delays. The effectiveness of this approach is demonstrated through a numerical illustration.




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