New Delay-Dependent Exponential $H_{\infty}$ Synchronization for Uncertain Neural Networks With Mixed Time Delays
作者： H.R. Karimi , Huijun Gao
DOI: 10.1109/TSMCB.2009.2024408
关键词: Artificial neural network 、 Control theory 、 Mathematics 、 Delay dependent 、 Discretization 、 Synchronization 、 Time delays 、 Exponential function 、 Control theory 、 Linear matrix
摘要: This paper establishes an exponential H infin synchronization method for a class of uncertain master and slave neural networks (MSNNs) with mixed time delays, where the delays comprise different neutral, discrete, distributed delays. The polytopic norm-bounded uncertainties are separately taken into consideration. An appropriate discretized Lyapunov-Krasovskii functional some free-weighting matrices utilized to establish delay-dependent sufficient conditions designing delayed state-feedback control as law in terms linear matrix inequalities under less restrictive conditions. controller guarantees two coupled MSNNs regardless their initial states. Detailed comparisons existing results made, numerical simulations carried out demonstrate effectiveness established laws.
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