New sets of criteria for exponential L2-L stability of Takagi-Sugeno fuzzy systems combined with hopfield neural networks

Choon Ki Ahn, Moon Kyou Song

Research output: Contribution to journalArticle

25 Citations (Scopus)

Abstract

In this paper, we propose new sets of criteria for exponential robust stability of Takagi-Sugeno (T-S) fuzzy Hopfield neural networks. The L2-L approach is applied to obtain new sets of stability criteria, under which T-S fuzzy Hopfield neural networks reduce the effect of external input to a prescribed level. These sets of criteria are presented based on the matrix norm and linear matrix inequality (LMI). The proposed sets of criteria also guarantee exponential stability for T-S fuzzy Hopfield neural networks without external input.

Original languageEnglish
Pages (from-to)2979-2986
Number of pages8
JournalInternational Journal of Innovative Computing, Information and Control
Volume9
Issue number7
Publication statusPublished - 2013

Keywords

  • Exponential L-L stability
  • Linear matrix inequality (LMI)
  • Matrix norm
  • Takagi-Sugeno (T-S) fuzzy hopfield neural network

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Information Systems
  • Computational Theory and Mathematics

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