Robust stability of stochastic genetic regulatory networks with disturbance attenuation

This paper presents a robust stability analysis approach to stochastic genetic regulatory networks with time-varying delays. By utilizing a Lyapunov functional and conducting stochastic analysis, we show that the addressed genetic regulatory networks are robustly asymptotically stable if a convex optimization problem is feasible. New stability criteria are derived to guarantee the addressed genetic regulatory networks to be robustly asymptotically stable. And these stability criteria are formulated by means of the feasibility of a linear matrix inequality (LMI), which can be effectively solved by some standard numerical packages. Illustrative example is given to show the usefulness of the proposed robust stability criteria.