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 Jul 17

Fingerprint

L-stability
Hopfield neural networks
Takagi-Sugeno Fuzzy Systems
Hopfield Neural Network
Fuzzy neural networks
Fuzzy systems
Exponential Stability
Stability criteria
Asymptotic stability
Linear matrix inequalities
Matrix Norm
Robust Stability
Stability Criteria
Matrix Inequality
Linear Inequalities

Keywords

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

ASJC Scopus subject areas

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

Cite this

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