Sampled-data state estimation for genetic regulatory networks with time-varying delays

被引：37

作者：

Anbuvithya, R. ^{[1
]}

Mathiyalagan, K. ^{[2
]}

Sakthivel, R. ^{[3
,4
]}

Prakash, P. ^{[1
]}

机构：

[1] Periyar Univ, Dept Math, Salem 636011, India

[2] Anna Univ Reg Ctr, Dept Math, Coimbatore 641047, Tamil Nadu, India

[3] Sri Ramakrishna Inst Technol, Dept Math, Coimbatore 641010, Tamil Nadu, India

[4] Sungkyunkwan Univ, Dept Math, Suwon 440746, South Korea

来源：

NEUROCOMPUTING | 2015年 / 151卷

关键词：

Genetic regulatory networks; Sampled-data; State estimation; Feedback regulation; Time-varying delays; STOCHASTIC STABILITY ANALYSIS; NEURAL-NETWORKS; DESIGN;

D O I：

10.1016/j.neucom.2014.10.029

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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-Krasovslcii 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. (C) 2014 Elsevier B.V. All rights reserved.

引用

页码：737 / 744

页数：8

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