Global asymptotic stability of stochastic fuzzy cellular neural networks with multiple time-varying delays
EXPERT SYSTEMS WITH APPLICATIONS, vol.37, no.12, pp.7737-7744, 2010 (SCI-Expanded, Scopus)
- Publication Type: Article / Article
- Volume: 37 Issue: 12
- Publication Date: 2010
- Doi Number: 10.1016/j.eswa.2010.04.067
- Journal Name: EXPERT SYSTEMS WITH APPLICATIONS
- Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
- Page Numbers: pp.7737-7744
- Keywords: Fuzzy cellular neural networks, Global asymptotic stability, Linear matrix inequality, Lyapunov functional, Multiple time-varying delays, EXPONENTIAL STABILITY, ROBUST STABILITY, LMI APPROACH, SYSTEMS
- Istanbul University-Cerrahpasa Affiliated: Yes
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. (C) 2010 Elsevier Ltd. All rights reserved.