Improved exponential convergence result for generalized neural networks including interval time-varying delayed signals

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

Research output: Contribution to journalArticle

28 Citations (Scopus)

Abstract

This article examines the exponential stability analysis problem of generalized neural networks (GNNs) including interval time-varying delayed states. A new improved exponential stability criterion is presented by establishing a proper Lyapunov–Krasovskii functional (LKF) and employing new analysis theory. The improved reciprocally convex combination (RCC) and weighted integral inequality (WII) techniques are utilized to obtain new sufficient conditions to ascertain the exponential stability result of such delayed GNNs. The superiority of the obtained results is clearly demonstrated by numerical examples.

Original languageEnglish
Pages (from-to)10-17
Number of pages8
JournalNeural Networks
Volume86
DOIs
Publication statusPublished - 2017 Feb 1

Keywords

  • Generalized neural network
  • Stability analysis
  • Time-varying delay
  • Weighted integral inequality

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Artificial Intelligence

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