State estimation of genetic regulatory networks under new dynamic event-triggered mechanism

Abstract

In this article, we investigate the state estimation problem for discrete-time genetic regulatory networks with timevarying delays and Markovian jumping parameters. A new dynamic event-triggered mechanism is developed to adjust the measurement data releases. A new Markovian chain model is proposed to describe the parameter jumping, of which the transition probabilities are dependent on another stochastic variable with known sojourn probabilities. To ensure stochastic stability with disturbance attenuation level $\gamma$, a proper Lyapunov functional is designed, and certain conditions are given. In terms of the solutions to various matrix inequalities, the desired estimator parameters are derived. Finally, a simulation example is employed to demonstrate the effectiveness of the event-triggered state estimation techniques described in this paper.