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Event-triggered dissipative filtering for network-based stochastic genetic regulatory networks under aperiodic sampling

journal contribution
posted on 17.11.2020, 00:00 authored by Jia WangJia Wang, Yufeng LinYufeng Lin
© 2020 IEEE. Based on event-triggered mechanism, networked dissipative filtering of stochastic genetic regulatory networks is investigated under aperiodic sampling. The states of the genetic regulatory network are sampled aperiodically and transmitted via a communication network to filters to estimate the expression levels of the mRNA and protein. In order to make better use of limited communication resources, a novel communication scheme is proposed. Then considering both network-induced delays and aperiodic sampling simultaneously, the filtering error dynamics are modeled in the form of a stochastic system with a time-varying delay. By Lyapunov theory and Wirtinger-based integral inequalities in a stochastic setting, asymptotical stability and dissipativity of the error dynamic system can be ensured. Based on the derived criterion, suitable dissipative filters are designed such that a set of inequalities are satisfied. Finally, the effectiveness of the proposed method is illustrated by a simulation example.

History

Volume

8

Start Page

23246

End Page

23254

eISSN

2169-3536

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Additional Rights

CC BY 4.0

Peer Reviewed

Yes

Open Access

Yes

Acceptance Date

08/01/2020

External Author Affiliations

Fuzhou University, China

Author Research Institute

Centre for Intelligent Systems

Era Eligible

Yes

Journal

IEEE Access