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 journalArticle

24 Citations (Scopus)

Abstract

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
JournalIEEE Transactions on Neural Networks and Learning Systems
DOIs
Publication statusAccepted/In press - 2018 May 22

Keywords

  • Artificial neural networks
  • Asymptotic stability
  • Circuit stability
  • Delays
  • Discrete-time neural networks (DNNs)
  • finite-time passivity (FTP) analysis
  • Lyapunov method
  • Numerical stability
  • Stability criteria
  • weighted summation inequality.

ASJC Scopus subject areas

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

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