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

Research output: Contribution to journalArticle

37 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

Fingerprint

Hopfield neural networks
Hopfield Neural Network
Fuzzy neural networks
Asymptotical Stability
Passivity
Lyapunov Stability Theory
Time Delay
Demonstrate
Time delay
Learning

Keywords

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

ASJC Scopus subject areas

  • Artificial Intelligence
  • Logic

Cite this

Some new results on stability of Takagi-Sugeno fuzzy Hopfield neural networks. / Ahn, Choon Ki.

In: Fuzzy Sets and Systems, Vol. 179, No. 1, 16.09.2011, p. 100-111.

Research output: Contribution to journalArticle

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