TY - JOUR
T1 - Sampled-Data State Estimation of Reaction Diffusion Genetic Regulatory Networks via Space-Dividing Approaches
AU - Song, Xiaona
AU - Wang, Mi
AU - Song, Shuai
AU - Ahn, Choon Ki
N1 - Funding Information:
This work was supported in part by the National Natural Science Foundation of China under Grant U1604146, in part by the Foundation for the University Technological Innovative Talents of Henan Province under Grant 18HASTIT019, in part by the National Research Foundation of Korea through the Ministry of Science, ICT and Future Planning under Grant NRF-2017R1A1A1A05001325, and in part by the Brain Korea 21 Plus Project in 2019.
Publisher Copyright:
© 2004-2012 IEEE.
PY - 2021/3/1
Y1 - 2021/3/1
N2 - A novel state estimator is designed for genetic regulatory networks with reaction-diffusion terms in this study. First, the diffusion space (where mRNA and protein exist) is divided into several parts and only a point, a line, or a plane, etc., is measured in every subspace to reduce the measurement cost effectively. Then, samplers and network-induced time delay are considered to meet the network transmission requirement. A new criterion to ensure that the estimation error converges to zero is established by using the Lyapunov functional combined with Wirtinger's inequality, reciprocally convex approach, and Halanay's inequality; furthermore, the estimator's parameters are derived by solving linear matrix inequalities. Finally, two simulation examples (including one-dimensional and two-dimensional spaces) are presented to demonstrate the developed scheme's applicability.
AB - A novel state estimator is designed for genetic regulatory networks with reaction-diffusion terms in this study. First, the diffusion space (where mRNA and protein exist) is divided into several parts and only a point, a line, or a plane, etc., is measured in every subspace to reduce the measurement cost effectively. Then, samplers and network-induced time delay are considered to meet the network transmission requirement. A new criterion to ensure that the estimation error converges to zero is established by using the Lyapunov functional combined with Wirtinger's inequality, reciprocally convex approach, and Halanay's inequality; furthermore, the estimator's parameters are derived by solving linear matrix inequalities. Finally, two simulation examples (including one-dimensional and two-dimensional spaces) are presented to demonstrate the developed scheme's applicability.
KW - Data sampling
KW - genetic regulatory networks
KW - reaction-diffusion terms
KW - space-dividing
KW - state estimation
UR - http://www.scopus.com/inward/record.url?scp=85076360972&partnerID=8YFLogxK
U2 - 10.1109/TCBB.2019.2919532
DO - 10.1109/TCBB.2019.2919532
M3 - Article
C2 - 31150343
AN - SCOPUS:85076360972
SN - 1545-5963
VL - 18
SP - 718
EP - 730
JO - IEEE/ACM Transactions on Computational Biology and Bioinformatics
JF - IEEE/ACM Transactions on Computational Biology and Bioinformatics
IS - 2
M1 - 8723523
ER -