Event-Triggered Dissipative Filtering for Network-Based Stochastic Genetic Regulatory Networks Under Aperiodic Sampling

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.