کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
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412056 | 679608 | 2015 | 8 صفحه PDF | دانلود رایگان |
This study examines the sampled-data state estimation problem for genetic regulatory networks (GRNs) with time-varying delays. Instead of the continuous measurements, the sampled measurements are used to estimate the true concentration of mRNAs and proteins of the GRNs. By changing the sampling period into a bounded time-varying delay, the error dynamics of the considered GRN is derived in terms of a dynamical system with time-varying delays. Sufficient conditions are derived such that the augmented system governing the error dynamics is globally asymptotically stable. The design of the desired state estimator is proposed by constructing a suitable Lyapunov–Krasovskii functional (LKF), and the design procedure can be easily achieved by solving a set of linear matrix inequalities (LMIs). Finally, the proposed method is validated through the numerical simulation which shows the effectiveness the our results.
Journal: Neurocomputing - Volume 151, Part 2, 5 March 2015, Pages 737–744