中国科学院自动化研究所机构知识库

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.