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

Authors:
P. Balasubramaniam;M. Syed Ali;Sabri Arik
Affiliations:
Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, Tamil Nadu, India;Department of Mathematics, Gandhigram Rural University, Gandhigram 624 302, Tamil Nadu, India;Department of Computer Engineering, Istanbul University, Turkey
Venue:
Expert Systems with Applications: An International Journal
Year:
2010

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Abstract

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.