Robust distributed state estimation for genetic regulatory networks with markovian jumping parameters

► The system parameters are norm-bounded and controlled by a Markov chain. ► A distributed state estimator is designed to approximates the genetic states. ► The criteria derived are in the form of the linear matrix inequalities (LMIs). In this paper, the robust distributed state estimation problem i...

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Bibliographic Details
Published in:Communications in nonlinear science & numerical simulation Vol. 16; no. 10; pp. 4060 - 4078
Main Authors: Lv, Bei, Liang, Jinling, Cao, Jinde
Format: Journal Article
Language:English
Published: Elsevier B.V 01.10.2011
Subjects:
Asymptotic properties
Estimators
Genetic regulatory networks (GRNs)
Genetics
Linear matrix inequalities (LMIs)
Logic
Markovian process
Mathematical models
Multiple sensors
Networks
Robust distributed state estimation
Sensors
State estimation
Time-varying delays
Time-varying delays
Markovian process
Multiple sensors
Linear matrix inequalities (LMIs)
Genetic regulatory networks (GRNs)
Robust distributed state estimation
ISSN:1007-5704, 1878-7274
Online Access:Get full text
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Summary:► The system parameters are norm-bounded and controlled by a Markov chain. ► A distributed state estimator is designed to approximates the genetic states. ► The criteria derived are in the form of the linear matrix inequalities (LMIs). In this paper, the robust distributed state estimation problem is dealt with for the delayed genetic regulatory networks (GRNs) with SUM logic and multiple sensors. The system parameters are time-varying, norm-bounded, and controlled by a Markov Chain. Time delays here are assumed to be time-varying and belong to the given intervals. The genetic regulatory functions are supposed to satisfy the sector-like condition. We aim to design a distributed state estimator which approximates the genetic states through the measurements of the sensors, i.e., the estimation error system is robustly asymptotically stable in the mean square. Based on the Lyapunov functional method and the stochastic analysis technique, it is shown that if a set of linear matrix inequalities (LMIs) are feasible, the desired distributed state estimator does exist. A numerical example is constructed in the end of the paper to demonstrate the effectiveness of the obtained criteria.
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ISSN:1007-5704
1878-7274
DOI:10.1016/j.cnsns.2011.02.009