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

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 imp...

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Bibliographic Details
Published inAIMS mathematics Vol. 6; no. 1; pp. 998 - 1023
Main Authors Prasertsang, Patarawadee, Botmart, Thongchai
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2021
Subjects
finite-time stability
integral inequality
neural networks
time-varying delays
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Summary: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.
ISSN:2473-6988
2473-6988
DOI:10.3934/math.2021060