Passive learning and input-to-state stability of switched Hopfield neural networks with time-delay

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In this paper, we propose a new passive weight learning law for switched Hopfield neural networks with time-delay under parametric uncertainty. Based on the proposed passive learning law, some new stability results, such as asymptotical stability, input-to-state stability (ISS), and bounded input-bounded output (BIBO) stability, are presented. An existence condition for the passive weight learning law of switched Hopfield neural networks is expressed in terms of strict linear matrix inequality (LMI). Finally, numerical examples are provided to illustrate our results.

Original languageEnglish
Pages (from-to)4582-4594
Number of pages13
JournalInformation Sciences
Issue number23
Publication statusPublished - 2010 Dec 1
Externally publishedYes



  • Input-to-state stability (ISS)
  • Linear matrix inequality (LMI)
  • Lyapunov-Krasovskii stability theory
  • Passive weight learning law
  • Switched Hopfield neural networks

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Science Applications
  • Software
  • Information Systems and Management
  • Control and Systems Engineering
  • Theoretical Computer Science

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