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Generalized Halanay Inequalities and Asymptotic Behavior of Nonautonomous Neural Networks with Infinite Delays

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  • Dehao Ruan

    (School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

  • Yao Lu

    (School of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou 510665, China)

Abstract

This paper focuses on the asymptotic behavior of nonautonomous neural networks with delays. We establish criteria for analyzing the asymptotic behavior of nonautonomous recurrent neural networks with delays by means of constructing some new generalized Halanay inequalities. We do not require to constructi any complicated Lyapunov function and our results improve some existing works. Lastly, we provide some illustrative examples to demonstrate the effectiveness of the obtained results.

Suggested Citation

  • Dehao Ruan & Yao Lu, 2024. "Generalized Halanay Inequalities and Asymptotic Behavior of Nonautonomous Neural Networks with Infinite Delays," Mathematics, MDPI, vol. 12(1), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:1:p:155-:d:1312506
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    References listed on IDEAS

    as
    1. Jiang, Haijun & Teng, Zhidong, 2006. "Boundedness and global stability for nonautonomous recurrent neural networks with distributed delays," Chaos, Solitons & Fractals, Elsevier, vol. 30(1), pages 83-93.
    2. Zhang, Qiang & Wei, Xiaopeng & Xu, Jin, 2009. "Global exponential stability for nonautonomous cellular neural networks with unbounded delays," Chaos, Solitons & Fractals, Elsevier, vol. 39(3), pages 1144-1151.
    3. Song, Qiankun & Zhao, Zhenjiang, 2005. "Global dissipativity of neural networks with both variable and unbounded delays," Chaos, Solitons & Fractals, Elsevier, vol. 25(2), pages 393-401.
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