Stability criteria for stochastic Takagi-Sugeno fuzzy Cohen-Grossberg BAM neural networks with mixed time-varying delays

Muhammed Syed Ali, Pagavathigounder Balasubramaniam, Fathalla A. Rihan, Shanmugam Lakshmanan

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)

Abstract

This article is concerned with the asymptotic stability analysis of Takagi-Sugeno stochastic fuzzy Cohen-Grossberg neural networks with discrete and distributed time-varying delays. Based on the Lyapunov functional and linear matrix inequality (LMI) technique, sufficient conditions are derived to ensure the global convergence of the equilibrium point. The proposed conditions can be checked easily by LMI Control Toolbox in Matlab. It has been shown that the results are less restrictive than previously known criteria. They are obtained under mild conditions, assuming neither differentiability nor strict monotonicity for activation function. Numerical examples are given to demonstrate the effectiveness of our results.

Original languageEnglish
Pages (from-to)143-154
Number of pages12
JournalComplexity
Volume21
Issue number5
DOIs
Publication statusPublished - May 1 2016

Keywords

  • Cohen-Grossberg bidirectional associative memory neural network
  • Global asymptotic stability
  • Linear matrix inequality
  • Lyapunov functional
  • Stochastic analysis
  • T-S fuzzy model
  • Time-varying delays

ASJC Scopus subject areas

  • General

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