Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks

Research output: Contribution to journalArticle

38 Citations (Scopus)

Abstract

In this paper, we propose some new results on stability properties of Takagi-Sugeno fuzzy Hopfield neural networks with time-delay. Based on Lyapunov stability theory, a new learning law is derived to guarantee passivity and asymptotical stability of Takagi-Sugeno fuzzy Hopfield neural networks. Furthermore, a new condition for input-to-state stability (ISS) is established. Illustrative examples are given to demonstrate the effectiveness of the proposed results.

Original languageEnglish
Pages (from-to)100-111
Number of pages12
JournalFuzzy Sets and Systems
Volume179
Issue number1
DOIs
Publication statusPublished - 2011 Sep 16
Externally publishedYes

Keywords

  • Input-to-state stability (ISS)
  • Learning
  • Lyapunov stability theory
  • Neuro-fuzzy systems
  • Passivity

ASJC Scopus subject areas

  • Logic
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks'. Together they form a unique fingerprint.

  • Cite this