Finite-Time Passivity-Based Stability Criteria for Delayed Discrete-Time Neural Networks via New Weighted Summation Inequalities

Ramasamy Saravanakumar, Sreten B. Stojanovic, Damnjan D. Radosavljevic, Choon Ki Ahn, Hamid Reza Karimi

Research output: Contribution to journalArticlepeer-review

58 Citations (Scopus)


In this paper, we study the problem of finite-time stability and passivity criteria for discrete-time neural networks (DNNs) with variable delays. The main objective is how to effectively evaluate the finite-time passivity conditions for NNs. To achieve this, some new weighted summation inequalities are proposed for application to a finite-sum term appearing in the forward difference of a novel Lyapunov-Krasovskii functional, which helps to ensure that the considered delayed DNN is passive. The derived passivity criteria are presented in terms of linear matrix inequalities. A numerical example is given to illustrate the effectiveness of the proposed results.

Original languageEnglish
Article number8362787
Pages (from-to)58-71
Number of pages14
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number1
Publication statusPublished - 2019 Jan

Bibliographical note

Funding Information:
Manuscript received August 9, 2017; revised January 12, 2018 and March 27, 2018; accepted April 9, 2018. Date of publication May 22, 2018; date of current version December 19, 2018. This work was supported in part by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325, in part by the Brain Korea 21 Plus Project in 2018, and in part by the Ministry of Science and Technology of Serbia under Grant ON174001. (Corresponding author: Choon Ki Ahn.) R. Saravanakumar is with the Department of Mathematics, Faculty of Science, Mahidol University, Bangkok 10400, Thailand, and also with the Research Center for Wind Energy Systems, Kunsan National University, Gunsan-si 54005, South Korea (e-mail:

Publisher Copyright:
© 2012 IEEE.


  • Discrete-time neural networks (DNNs)
  • Lyapunov method
  • finite-time passivity (FTP) analysis
  • weighted summation inequality

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Computer Networks and Communications
  • Artificial Intelligence


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