Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time-varying delays


BALASUBRAMANIAM P., ALI M. S. , Arik S.

EXPERT SYSTEMS WITH APPLICATIONS, cilt.37, ss.7737-7744, 2010 (SCI İndekslerine Giren Dergi) identifier identifier

  • Cilt numarası: 37 Konu: 12
  • Basım Tarihi: 2010
  • Doi Numarası: 10.1016/j.eswa.2010.04.067
  • Dergi Adı: EXPERT SYSTEMS WITH APPLICATIONS
  • Sayfa Sayıları: ss.7737-7744

Özet

In this paper, the Takagi-Sugeno (T-S) fuzzy model representation is extended to the stability analysis for stochastic cellular neural networks with multiple time-varying delays using linear matrix inequality (LMI) theory. A novel LMI-based stability criterion is derived to guarantee the asymptotic stability of stochastic cellular neural networks with multiple time-varying delays which are represented by T-S fuzzy models. In order to derive delay-dependent stability conditions, free-weighting matrices method has been introduced, which may develop less-conservative results. In fact, these techniques lead to generalized and less-conservative stability condition that guarantee the wide stability region. Our results can be specialized to several cases including those studied extensively in the literature. Finally, numerical examples are given to demonstrate the effectiveness and conservativeness of our results. (C) 2010 Elsevier Ltd. All rights reserved.