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Neural Computing and Applications
Published in: Neural Computing and Applications 5/2013

01-10-2013 | Original Article

Robust stability of stochastic genetic regulatory networks with time-varying delays: a delay fractioning approach

Authors: Yantao Wang, Ahui Yu, Xian Zhang

Published in: Neural Computing and Applications | Issue 5/2013

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Abstract

This paper is concerned with the problem of the globally asymptotically mean square stability for a class of delayed genetic regularity networks (GRNs) with both parameter uncertainties and stochastic disturbances, where the time delays are belong to given intervals and assumed to be time varying. Based on choosing an appropriate and novel Lyapunov functional, a “delay fractioning” approach that is different from the existing ones is introduced. By utilizing \(It\hat{o}\hbox{'}s\) differential formula and using the linear matrix inequality (LMI) method, we derive a robust asymptotical stability criterion in mean square sense for uncertain GRNs with time-varying delays. All the stability conditions are given in terms of LMIs. One example and its simulation are provided to show the advantages of the obtained result.
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Metadata
Title
Robust stability of stochastic genetic regulatory networks with time-varying delays: a delay fractioning approach
Authors
Yantao Wang
Ahui Yu
Xian Zhang
Publication date
01-10-2013
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 5/2013
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-012-1034-y

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