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Robust stability of stochastic fuzzy impulsive recurrent neural networks with\\ time-varying delays | ||
| Iranian Journal of Fuzzy Systems | ||
| مقاله 2، دوره 11، شماره 4، آبان 2014، صفحه 1-13 اصل مقاله (402.91 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22111/ijfs.2014.1620 | ||
| نویسنده | ||
| M. Syed Ali* | ||
| Department of Mathematics, Thiruvalluvar University, Vellore - 632 106, Tamilnadu, India | ||
| چکیده | ||
| In this paper, global robust stability of stochastic impulsive recurrent neural networks with time-varying delays 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-varying delays. 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. | ||
| کلیدواژهها | ||
| Global asymptotic stability؛ Impulsive perturbations؛ Stochastic fuzzy recurrent neural networks؛ Time-varying delays | ||
| مراجع | ||
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