Sampled-Data State Estimation of Reaction Diffusion Genetic Regulatory Networks via Space-Dividing Approaches
- Authors
- Song, Xiaona; Wang, Mi; Song, Shuai; Ahn, Choon Ki
- Issue Date
- 3월-2021
- Publisher
- IEEE COMPUTER SOC
- Keywords
- Proteins; Aerospace electronics; State estimation; Genetics; Stability analysis; Extraterrestrial measurements; Linear matrix inequalities; Data sampling; genetic regulatory networks; reaction-diffusion terms; state estimation; space-dividing
- Citation
- IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS, v.18, no.2, pp.718 - 730
- Indexed
- SCIE
SCOPUS
- Journal Title
- IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS
- Volume
- 18
- Number
- 2
- Start Page
- 718
- End Page
- 730
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/128463
- DOI
- 10.1109/TCBB.2019.2919532
- ISSN
- 1545-5963
- Abstract
- 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.
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