Stability of markovian jump generalized neural networks with interval time-varying delays

Ramasamy Saravanakumar, Muhammed Syed Ali, Choon Ki Ahn, Hamid Reza Karimi, Peng Shi

Research output: Contribution to journalArticlepeer-review

108 Citations (Scopus)


This paper examines the problem of asymptotic stability for Markovian jump generalized neural networks with interval time-varying delays. Markovian jump parameters are modeled as a continuous-time and finite-state Markov chain. By constructing a suitable Lyapunov-Krasovskii functional (LKF) and using the linear matrix inequality (LMI) formulation, new delay-dependent stability conditions are established to ascertain the mean-square asymptotic stability result of the equilibrium point. The reciprocally convex combination technique, Jensen's inequality, and the Wirtinger-based double integral inequality are used to handle single and double integral terms in the time derivative of the LKF. The developed results are represented by the LMI. The effectiveness and advantages of the new design method are explained using five numerical examples.

Original languageEnglish
Article number7466838
Pages (from-to)1840-1850
Number of pages11
JournalIEEE Transactions on Neural Networks and Learning Systems
Issue number8
Publication statusPublished - 2017 Aug


  • Asymptotic stability
  • Markovian jump parameters
  • generalized neural networks (GNNs)
  • interval time-varying delay

ASJC Scopus subject areas

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


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