State estimation for genetic regulatory networks with time-varying delay using stochastic sampled-data

Author

Lee, Tong H. ; Park, M.J. ; Kwon, O.M. ; Park, Jae Hyo ; Lee, S.M.

Author_Institution

Dept. of Electr. Eng., Yeungnam Univ., Kyongsan, South Korea

fYear

2013

fDate

23-26 June 2013

Firstpage

1

Lastpage

6

Abstract

This paper considers genetic regulatory networks with time-varying delay. By construction of a suitable Lyapunov-Krasovskii functional and utilization of stochastic sampled-data, a delay-dependent state estimation for the concerned systems is established in terms of linear matrix inequalities (LMIs) which can be easily solved by various effective optimization algorithms. One numerical example is given to illustrate the effectiveness of the proposed method.

Keywords

Lyapunov methods; delays; genetics; linear matrix inequalities; molecular biophysics; optimisation; state estimation; LMI; Lyapunov-Krasovskii functional; delay-dependent state estimation; genetic regulatory networks; linear matrix inequalities; optimization algorithms; stochastic sampled-data utilization; time-varying delay; Control systems; Delays; Educational institutions; Mathematical model; Proteins; State estimation; Stochastic processes; Genetic regulatory networks; State estimator; Stochastic sampled-data; Time-varying delay;

fLanguage

English

Publisher

ieee

Conference_Titel

Control Conference (ASCC), 2013 9th Asian

Conference_Location

Istanbul

Print_ISBN

978-1-4673-5767-8

Type

conf

DOI

10.1109/ASCC.2013.6606371

Filename

6606371