Paper Info
Title
Finite-Time <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math><alternatives><mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"><mml:msub><mml:mi mathvariant="script">H</mml:mi><mml:mi>∞</mml:mi></mml:msub></mml:math><inline-graphic xlink:href="park-ieq1-3040979.gif" xmlns:xlink="http://www.w3.org/1999/xlink"/></alternatives></inline-formula> State Estimation for PDT-Switched Genetic Regulatory Networks With Randomly Occurring Uncertainties
Abstract
This article is concerned with the problem of finite-time <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> state estimation for switched genetic regulatory networks with randomly occurring uncertainties. The persistent dwell-time switching rule, as a more versatile class of switching rules, is considered in this paper. Besides, several random variables that obey the Bernoulli distribution are used to represent randomly occurring uncertainties. The overriding purpose of this article is to design an estimator to ensure that the estimation error system is stochastically finite-time bounded and satisfies the <inline-formula><tex-math notation="LaTeX">$\mathcal {H}_{\infty }$</tex-math></inline-formula> performance. Based on this, sufficient conditions for the explicit form of the estimator gains can be obtained by the Lyapunov method. Finally, a numerical example is given to verify the correctness and feasibility of the proposed method.
Year
DOI
Venue
2022
10.1109/TCBB.2020.3040979
IEEE/ACM Transactions on Computational Biology and Bioinformatics
Keywords
DocType
Volume
Gene Regulatory Networks,Neural Networks, Computer,Time Factors,Triazenes,Uncertainty
Journal
19
Issue
ISSN
Citations 
3
1545-5963
2
PageRank 
References 
Authors
0.35
30
5
Name
Order
Citations
PageRank
Jing Wang150793.00
Haitao Wang253836.95
Hao Shen3131.30
Bing Wang413815.87
Ju H. Park55878330.37