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

Tae H. Lee; M. J. Park; O. M. Kwon; Ju H. Park; S. M. Lee

doi:10.1109/ASCC.2013.6606371

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

Tae H. Lee, M. J. Park, O. M. Kwon, Ju H. Park, S. M. Lee

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

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.

Original language	English
Title of host publication	2013 9th Asian Control Conference, ASCC 2013
DOIs	https://doi.org/10.1109/ASCC.2013.6606371
Publication status	Published - 2013
Event	2013 9th Asian Control Conference, ASCC 2013 - Istanbul, Turkey Duration: 23 Jun 2013 → 26 Jun 2013

Publication series

Name	2013 9th Asian Control Conference, ASCC 2013

Conference

Conference	2013 9th Asian Control Conference, ASCC 2013
Country/Territory	Turkey
City	Istanbul
Period	23/06/13 → 26/06/13

Keywords

Genetic regulatory networks
State estimator
Stochastic sampled-data
Time-varying delay

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10.1109/ASCC.2013.6606371

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@inproceedings{763b5588d6cf43e293199a95909b6daf,

title = "State estimation for genetic regulatory networks with time-varying delay using stochastic sampled-data",

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 = "Genetic regulatory networks, State estimator, Stochastic sampled-data, Time-varying delay",

author = "Lee, {Tae H.} and Park, {M. J.} and Kwon, {O. M.} and Park, {Ju H.} and Lee, {S. M.}",

year = "2013",

doi = "10.1109/ASCC.2013.6606371",

language = "English",

isbn = "9781467357692",

series = "2013 9th Asian Control Conference, ASCC 2013",

booktitle = "2013 9th Asian Control Conference, ASCC 2013",

}

Lee, TH, Park, MJ, Kwon, OM, Park, JH & Lee, SM 2013, State estimation for genetic regulatory networks with time-varying delay using stochastic sampled-data. in 2013 9th Asian Control Conference, ASCC 2013., 6606371, 2013 9th Asian Control Conference, ASCC 2013, 2013 9th Asian Control Conference, ASCC 2013, Istanbul, Turkey, 23/06/13. https://doi.org/10.1109/ASCC.2013.6606371

TY - GEN

T1 - State estimation for genetic regulatory networks with time-varying delay using stochastic sampled-data

AU - Lee, Tae H.

AU - Park, M. J.

AU - Kwon, O. M.

AU - Park, Ju H.

AU - Lee, S. M.

PY - 2013

Y1 - 2013

N2 - 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.

AB - 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.

KW - Genetic regulatory networks

KW - State estimator

KW - Stochastic sampled-data

KW - Time-varying delay

UR - http://www.scopus.com/inward/record.url?scp=84886563303&partnerID=8YFLogxK

U2 - 10.1109/ASCC.2013.6606371

DO - 10.1109/ASCC.2013.6606371

M3 - Conference contribution

AN - SCOPUS:84886563303

SN - 9781467357692

T3 - 2013 9th Asian Control Conference, ASCC 2013

BT - 2013 9th Asian Control Conference, ASCC 2013

T2 - 2013 9th Asian Control Conference, ASCC 2013

Y2 - 23 June 2013 through 26 June 2013

ER -

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

Abstract

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Conference

Keywords

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