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Less conservative stability criteria for general neural networks through novel delay-dependent functional

Author

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  • Lee, S.H.
  • Park, M.J.
  • Kwon, O.M.
  • Choi, S.G.

Abstract

This work investigates the improved stability conditions for neural networks with time-varying delay. By the construction of newly augmented Lyapunov–Krasovskii functionals including the integral inequality and the use of the augmented zero equality approach, three improved results are proposed in the form of linear matrix inequalities. Two delay-dependent Lyapunov–Krasovskii functionals based on the integral inequality are proposed for the first time. Also, by utilizing the augmented zero equality approach, a less conservative result is obtained while reducing computation complexity. Through some numerical examples, the effectiveness and superiority of the proposed results are confirmed by comparing the existing works.

Suggested Citation

  • Lee, S.H. & Park, M.J. & Kwon, O.M. & Choi, S.G., 2022. "Less conservative stability criteria for general neural networks through novel delay-dependent functional," Applied Mathematics and Computation, Elsevier, vol. 420(C).
    • Handle: RePEc:eee:apmaco:v:420:y:2022:i:c:s0096300321009693
    DOI: 10.1016/j.amc.2021.126886
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    References listed on IDEAS

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    1. Lee, Tae H. & Park, Myeong Jin & Park, Ju H., 2021. "An improved stability criterion of neural networks with time-varying delays in the form of quadratic function using novel geometry-based conditions," Applied Mathematics and Computation, Elsevier, vol. 404(C).
    2. 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.
    3. Xu, Changjin & Liao, Maoxin & Li, Peiluan & Guo, Ying & Xiao, Qimei & Yuan, Shuai, 2019. "Influence of multiple time delays on bifurcation of fractional-order neural networks," Applied Mathematics and Computation, Elsevier, vol. 361(C), pages 565-582.
    4. Liu, Hailin & Chen, Guohua, 2007. "Delay-dependent stability for neural networks with time-varying delay," Chaos, Solitons & Fractals, Elsevier, vol. 33(1), pages 171-177.
    5. Xu, Changjin & Liu, Zixin & Yao, Lingyun & Aouiti, Chaouki, 2021. "Further exploration on bifurcation of fractional-order six-neuron bi-directional associative memory neural networks with multi-delays," Applied Mathematics and Computation, Elsevier, vol. 410(C).
    6. Kwon, O.M. & Lee, S.H. & Park, M.J. & Lee, S.M., 2020. "Augmented zero equality approach to stability for linear systems with time-varying delay," Applied Mathematics and Computation, Elsevier, vol. 381(C).
    7. Lee, Seok Young & Lee, Won Il & Park, PooGyeon, 2017. "Improved stability criteria for linear systems with interval time-varying delays: Generalized zero equalities approach," Applied Mathematics and Computation, Elsevier, vol. 292(C), pages 336-348.
    8. Zeng, Hong-Bing & Liu, Xiao-Gui & Wang, Wei, 2019. "A generalized free-matrix-based integral inequality for stability analysis of time-varying delay systems," Applied Mathematics and Computation, Elsevier, vol. 354(C), pages 1-8.
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