Research article Special Issues

Improvement of finite-time stability for delayed neural networks via a new Lyapunov-Krasovskii functional

  • Received: 04 September 2020 Accepted: 03 November 2020 Published: 06 November 2020
  • MSC : 34D20, 34K20, 37C75

  • The topic of finite-time stability criterion for neural networks with time-varying delays via a new argument Lyapunov-Krasovskii functional (LKF) was proposed and the time-varying delay of the system is without differentiable. For sufficient conditions of this study, a new (LKF) is combined with improved triple integral terms, namely the functionality of finite-time stability, integral inequality, and a positive diagonal matrix without using a free weighting matrix. The improved finite-time sufficient conditions for the neural network with time varying delay are given in terms of linear matrix inequalities (LMIs) and the results show improvement on previous studies. Numerical examples are given to illustrate the effectiveness of the proposed method.

    Citation: Patarawadee Prasertsang, Thongchai Botmart. Improvement of finite-time stability for delayed neural networks via a new Lyapunov-Krasovskii functional[J]. AIMS Mathematics, 2021, 6(1): 998-1023. doi: 10.3934/math.2021060

    Related Papers:

  • The topic of finite-time stability criterion for neural networks with time-varying delays via a new argument Lyapunov-Krasovskii functional (LKF) was proposed and the time-varying delay of the system is without differentiable. For sufficient conditions of this study, a new (LKF) is combined with improved triple integral terms, namely the functionality of finite-time stability, integral inequality, and a positive diagonal matrix without using a free weighting matrix. The improved finite-time sufficient conditions for the neural network with time varying delay are given in terms of linear matrix inequalities (LMIs) and the results show improvement on previous studies. Numerical examples are given to illustrate the effectiveness of the proposed method.


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