Article ID Journal Published Year Pages File Type
412056 Neurocomputing 2015 8 Pages PDF
Abstract

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.

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
Authors
, , , ,