On exponential stability results for fuzzy impulsive neural networks

  • Authors:
  • R. Rakkiyappan;P. Balasubramaniam

  • Affiliations:
  • Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, Tamilnadu, India;Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, Tamilnadu, India

  • Venue:
  • Fuzzy Sets and Systems
  • Year:
  • 2010

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Abstract

Complex nonlinear systems can be represented to a set of linear sub-models by using fuzzy sets and fuzzy reasoning via ordinary Takagi-Sugeno (TS) fuzzy models. In this paper, the exponential stability of TS fuzzy neural networks with impulsive effect and time-varying delays is investigated. The model for fuzzy impulsive neural networks with time-varying delays is first established as a modified TS fuzzy model in which the consequent parts are composed of a set of impulsive neural networks with time-varying delays. Secondly, the exponential stability for fuzzy impulsive neural networks is presented by utilizing the Lyapunov-Krasovskii functional and the linear matrix inequality (LMI) approach. In addition, two numerical examples are provided to illustrate the applicability of the result using LMI control toolbox in MATLAB.