Robust stability of stochastic fuzzy impulsive recurrent neural networks with\ time-varying delays
نویسنده
چکیده مقاله:
In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the Takagi-Sugeno (T-S) fuzzy models is considered. A novel Linear Matrix Inequality (LMI)-based stability criterion is obtained by using Lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural networks with time-varyingdelays. The results are related to the size of delay and impulses.Finally, numerical examples and simulations are given to demonstrate the correctness of the theoretical results.
منابع مشابه
robust stability of stochastic fuzzy impulsive recurrent neural networks with time-varying delays
in this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varyingdelays which are represented by the takagi-sugeno (t-s) fuzzy models is considered. a novel linear matrix inequality (lmi)-based stability criterion is obtained by using lyapunov functional theory to guarantee the asymptotic stability of uncertain fuzzy stochastic impulsive recurrent neural...
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عنوان ژورنال
دوره 11 شماره 4
صفحات 1- 13
تاریخ انتشار 2014-08-30
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