Stability analysis for delayed Hopfield neural networks

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, an ℋ approach is used to derive a tuning algorithm for delayed Hopfield neural networks. Based on the Lyapunov stability theory, the ℋ learning law is presented to not only guarantee asymptotical stability but also reduce the effect of an external disturbance to an ℋ norm constraint. An existence condition for the proposed learning law is represented in terms of a linear matrix inequality (LMI). An illustrative example is provided to demonstrate the effectiveness of the proposed learning law.

Original languageEnglish
Pages (from-to)203-208
Number of pages6
JournalProceedings of the Institution of Mechanical Engineers. Part I: Journal of Systems and Control Engineering
Volume224
Issue number2
DOIs
Publication statusPublished - 2010 Mar 1
Externally publishedYes

Keywords

  • Hopfield neural networks
  • Linear matrix inequality (LMI)
  • Lyapunov stability theory
  • Weight learning
  • ℋ approach

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Mechanical Engineering

Fingerprint Dive into the research topics of 'ℋ<sub>∞</sub>Stability analysis for delayed Hopfield neural networks'. Together they form a unique fingerprint.

  • Cite this