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Title of Article Finite-time passivity analysis of neutral-type neural networks with mixed time-varying delays 
Date of Acceptance 15 December 2021 
Journal
     Title of Journal Mathematics 
     Standard SCOPUS 
     Institute of Journal mdpi 
     ISBN/ISSN 2227-7390 
     Volume 2021 
     Issue 24 
     Month ธันวาคม
     Year of Publication 2023 
     Page  
     Abstract This research study investigates the issue of finite-time passivity analysis of neutral-type neural networks with mixed time-varying delays. The time-varying delays are distributed, discrete and neutral in that the upper bounds for the delays are available. We are investigating the creation of sufficient conditions for finite boundness, finite-time stability and finite-time passivity, which has never been performed before. First, we create a new Lyapunov–Krasovskii functional, Peng–Park’s integral inequality, descriptor model transformation and zero equation use, and then we use Wirtinger’s integral inequality technique. New finite-time stability necessary conditions are constructed in terms of linear matrix inequalities in order to guarantee finite-time stability for the system. Finally, numerical examples are presented to demonstrate the result’s effectiveness. Moreover, our proposed criteria are less conservative than prior studies in terms of larger time-delay bounds. 
     Keyword neural networks; finite-time passivity; linear matrix inequality; distributed delay; neutral system 
Author
627020048-6 Miss ISSARAPORN KHONCHAIYAPHUM [Main Author]
Science Doctoral Degree

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