Exponential stability, passivity, and dissipativity analysis of generalized neural networks with mixed time-varying delays

R. Saravanakumar, Grienggrai Rajchakit, Choon Ki Ahn, Hamid Reza Karimi

Research output: Contribution to journalArticlepeer-review

29 Citations (Scopus)

Abstract

In this paper, we analyze the exponential stability, passivity, and $\boldsymbol {(\mathfrak {Q},\mathfrak {S},\mathfrak {R})}$ - $\boldsymbol {\gamma }$ -dissipativity of generalized neural networks (GNNs) including mixed time-varying delays in state vectors. Novel exponential stability, passivity, and $\boldsymbol {(\mathfrak {Q},\mathfrak {S},\mathfrak {R})}$ - $\boldsymbol {\gamma }$ -dissipativity criteria are developed in the form of linear matrix inequalities for continuous-time GNNs by constructing an appropriate Lyapunov-Krasovskii functional (LKF) and applying a new weighted integral inequality for handling integral terms in the time derivative of the established LKF for both single and double integrals. Some special cases are also discussed. The superiority of employing the method presented in this paper over some existing methods is verified by numerical examples.

Original languageEnglish
Article number7979548
Pages (from-to)395-405
Number of pages11
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
Volume49
Issue number2
DOIs
Publication statusPublished - 2019 Feb

Keywords

  • (Q, S, R)-γ-dissipativity
  • Exponential passivity
  • Generalized neural networks (GNNs)
  • Time-varying delay
  • Weighted integral inequality (WII)

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering

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