Stability of stochastic genetic networks with both Markovian jumping parameters and mixed time delays

Author

Li Li ; Yongqing Yang ; Tian Liang ; Yang Liu

Author_Institution

Key Lab. of Adv. Process Control for Light Ind. (Minist. of Educ.), Jiangnan Univ., Wuxi, China

fYear

2012

fDate

18-20 Oct. 2012

Firstpage

521

Lastpage

526

Abstract

This paper investigates the issue of stability for stochastic genetic networks with both Markovian jumping parameters and mixed time delays. The jumping parameters are modelled as a continuous-time discrete-state Markovian chain. By constructing Lyapunov functional and using linear matrix inequality (LMI) techniques, sufficient conditions for genetic regulatory networks to be asymptotically stable in the mean square are derived. Two numerical examples are given to illustrate the effectiveness of our results.

Keywords

Lyapunov methods; Markov processes; asymptotic stability; biology; continuous time systems; delays; genetics; linear matrix inequalities; stochastic systems; LMI; Lyapunov functional; Markovian jumping parameters; asymptotic stability; continuous-time discrete-state Markovian chain; genetic regulatory networks; linear matrix inequality techniques; mixed time delays; stochastic genetic networks; Conferences;

fLanguage

English

Publisher

ieee

Conference_Titel

Advanced Computational Intelligence (ICACI), 2012 IEEE Fifth International Conference on

Conference_Location

Nanjing

Print_ISBN

978-1-4673-1743-6

Type

conf

DOI

10.1109/ICACI.2012.6463218

Filename

6463218