Robust stability analysis of Takagi-Sugeno uncertain stochastic fuzzy recurrent neural networks with

• 作 者: M.Syed Ali
• 机 构: Department of Mathematics,Thiruvalluvar University,Vellore-632 106,Tamilnadu,India,
• 出 处: Chinese Physics B
• 发表日期: 2011年08期
• 关键词: stochastic uncertain guarantee asymptotic removed requirement concerned proof de

In this paper,the global stability of Takagi-Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered.A novel LMI-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of TSUSFRNNs.The proposed stability conditions are demonstrated through numerical examples.Furthermore,the supplementary requirement that the time derivative of time-varying delays must be smaller than one is removed.Comparison results are demonstrated to show that the proposed method is more able to guarantee the widest stability region than the other methods available in the existing literature.

In this paper, the global stability of Takagi-Sugeno (TS) uncertain stochastic fuzzy recurrent neural networks with discrete and distributed time-varying delays (TSUSFRNNs) is considered. A novel LMI-based stability criterion is obtained by using Lyapunov functional theory guarantee the asymptotic stability of TSUSFRNNs.The proposed condition is demonstrated that a proposed method is more than one is removed. able to guarantee the widest stability region than the other methods available in the existing literature.