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 journalArticle

23 Citations (Scopus)

Abstract

In this paper, we analyze the exponential stability, passivity, and (Q,S,R)-ɣ-dissipativity of generalized neural networks (GNNs) including mixed time-varying delays in state vectors. Novel exponential stability, passivity, and (Q,S,R)-ɣ-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
JournalIEEE Transactions on Systems, Man, and Cybernetics: Systems
DOIs
Publication statusAccepted/In press - 2017 Jul 13

Keywords

  • (Q,S,R)-ɣ-dissipativity
  • Artificial neural networks
  • Asymptotic stability
  • Control theory
  • Delays
  • Exponential passivity
  • generalized neural networks (GNNs)
  • Stability criteria
  • time-varying delay
  • weighted integral inequality (WII)

ASJC Scopus subject areas

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

Fingerprint Dive into the research topics of 'Exponential Stability, Passivity, and Dissipativity Analysis of Generalized Neural Networks With Mixed Time-Varying Delays'. Together they form a unique fingerprint.

  • Cite this