Open AccessDelay‐dependent robust dissipative filtering of stochastic genetic regulatory networks with time‐varying delays
Author(s) -
Li Yanjiang,
Liu Guoping
Publication year - 2013
Publication title -
iet control theory and applications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.059
H-Index - 108
eISSN - 1751-8652
pISSN - 1751-8644
DOI - 10.1049/iet-cta.2013.0040
Subject(s) - control theory (sociology) , bounding overwatch , dissipative system , bounded function , mathematics , filter (signal processing) , upper and lower bounds , linear matrix inequality , stability theory , stability (learning theory) , norm (philosophy) , model transformation , computer science , mathematical optimization , nonlinear system , control (management) , mathematical analysis , physics , quantum mechanics , artificial intelligence , geometry , consistency (knowledge bases) , machine learning , political science , law , computer vision
This study deals with the problem of delay‐dependent dissipative filtering for genetic regulatory networks (GRNs) with norm‐bounded parameter uncertainties and time‐varying delays. It is assumed that the non‐linear function describing the feedback regulation satisfies the sector‐bounded condition. Improved delay‐dependent stochastic stability and filter design method for stochastic GRNs are obtained by applying the delay fractioning technique, Jensen inequalities and introducing some proper slack matrix variables. The conservatism caused by either model transformation or bounding techniques is reduced. Sufficient conditions of the robust stability and filtering problems are proposed, respectively. The filter, which can guarantee the resulting error system to be asymptotically stable in the mean‐square sense and satisfy a prescribed performance level for all delays no larger than a given upper bound, are constructed. A numerical example is provided to demonstrate effectiveness of the proposed results in this study.