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Stability Analysis of Delayed Neural Networks via Composite-Matrix-Based Integral Inequality

Author

Listed:
  • Yupeng Shi

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Dayong Ye

    (School of Mechanical Engineering and Automation, Northeastern University, Shenyang 110819, China)

Abstract

This paper revisits the problem of stability analyses for neural networks with time-varying delay. A composite-matrix-based integral inequality (CMBII) is presented, which takes the delay derivative into account. In this case, the coupling information can be fully captured in integral inequalities with the delay derivative. Based on a CMBII, a new stability criterion is derived for neural networks with time-varying delay. The effectiveness of this method is verified by a numerical example.

Suggested Citation

  • Yupeng Shi & Dayong Ye, 2023. "Stability Analysis of Delayed Neural Networks via Composite-Matrix-Based Integral Inequality," Mathematics, MDPI, vol. 11(11), pages 1-13, May.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:11:p:2518-:d:1159962
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    References listed on IDEAS

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    1. Zhang, Chuan-Ke & He, Yong & Jiang, Lin & Lin, Wen-Juan & Wu, Min, 2017. "Delay-dependent stability analysis of neural networks with time-varying delay: A generalized free-weighting-matrix approach," Applied Mathematics and Computation, Elsevier, vol. 294(C), pages 102-120.
    2. Jun Wang & Yongqiang Tian & Lanfeng Hua & Kaibo Shi & Shouming Zhong & Shiping Wen, 2023. "New Results on Finite-Time Synchronization Control of Chaotic Memristor-Based Inertial Neural Networks with Time-Varying Delays," Mathematics, MDPI, vol. 11(3), pages 1-18, January.
    3. Hayrengul Sadik & Abdujelil Abdurahman & Rukeya Tohti, 2023. "Fixed-Time Synchronization of Reaction-Diffusion Fuzzy Neural Networks with Stochastic Perturbations," Mathematics, MDPI, vol. 11(6), pages 1-15, March.
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    Cited by:

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