New stability criteria for Takagi-Sugeno fuzzy Hopfield neural networks

Research output: Contribution to journalArticle

Abstract

In this paper, new stability criteria are derived for Takagi-Sugeno (T-S) fuzzy Hopfield neural networks via the input/output-to-state stability (IOSS) approach. Based on matrix norm and linear matrix inequality (LMI), these stability criteria guarantee input/output-to-state stability for external input vector. Moreover, the criteria for asymptotic stability of T-S fuzzy Hopfield neural networks without external input vector are presented.

Original languageEnglish
Pages (from-to)237-246
Number of pages10
JournalInternational Journal of Pure and Applied Mathematics
Volume75
Issue number2
Publication statusPublished - 2012 Mar 12
Externally publishedYes

Fingerprint

Hopfield neural networks
Hopfield Neural Network
Fuzzy neural networks
Stability criteria
Stability Criteria
Matrix Norm
Output
Asymptotic stability
Linear matrix inequalities
Asymptotic Stability
Matrix Inequality
Linear Inequalities

Keywords

  • Input/output-to-state stability (IOSS)
  • Linear matrix inequality (LMI)
  • Takagi-Sugeno (T-S) fuzzy Hopfield neural networks

ASJC Scopus subject areas

  • Mathematics(all)
  • Applied Mathematics

Cite this

New stability criteria for Takagi-Sugeno fuzzy Hopfield neural networks. / Ahn, Choon Ki.

In: International Journal of Pure and Applied Mathematics, Vol. 75, No. 2, 12.03.2012, p. 237-246.

Research output: Contribution to journalArticle

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