Delay-interval dependent robust stability criteria for stochastic neural networks with linear fractional uncertainties
作者： P. Balasubramaniam , S. Lakshmanan , R. Rakkiyappan
DOI: 10.1016/J.NEUCOM.2009.06.006
关键词: Stochastic neural network 、 Control theory 、 Mathematics 、 Function (mathematics) 、 Stochastic stability 、 Linear matrix 、 Stability (probability) 、 Interval (mathematics)
摘要: In this paper, we study the delay-interval dependent robust stability criteria for stochastic neural networks with linear fractional uncertainties. The time-varying delay is assumed to belong an interval and a fast function. uncertainty under consideration includes norm-bounded uncertainty. Based on new Lyapunov-Krasovskii functional, some inequality techniques theory, are obtained in terms of matrix inequalities. Finally, numerical examples provided demonstrate less conservatism effectiveness proposed LMI conditions.
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