Open AccessDistributed nonsynchronous event-triggered state estimation of genetic regulatory networks with hidden Markovian jumping parameters
Author(s) -
Chao Ma,
Yanfeng Lu
Publication year - 2022
Publication title -
mathematical biosciences and engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.451
H-Index - 45
eISSN - 1551-0018
pISSN - 1547-1063
DOI - 10.3934/mbe.2022647
Subject(s) - computer science , jumping , state (computer science) , markov process , estimation , event (particle physics) , hidden markov model , control theory (sociology) , mode (computer interface) , artificial neural network , mathematical optimization , mathematics , artificial intelligence , algorithm , control (management) , engineering , biology , statistics , physiology , physics , systems engineering , quantum mechanics , operating system
In this paper, the distributed state estimation problem of genetic regulatory networks (GRNs) with hidden Markovian jumping parameters (HMJPs) is explored. Furthermore, in order to improve the communication efficiency among state estimation sensors, the event-triggered strategy is employed in the distributed framework for sensor networks. Particularly, by considering the fact that the true modes are always unaccessible, a novel nonsynchronous state estimation (NSE) strategy is utilized based on observed hidden mode information. By means of Lyapunov-Krasovski method, sufficient stochastic state estimation analysis and synthesis results are established, such that the concentrations of mRNA and protein in GRNs can be both well estimated by convex optimization. Finally, an illustrative example with relevant simulations results is provided to validate the applicability and effectiveness of the developed state estimation approach.