Non-fragile state estimation for reaction-diffusion genetic regulatory networks with mode-dependent timevarying delays

This paper investigates the problem of non-fragile state estimation for a class of reaction-diffusion genetic regulatory networks with mode-dependent time-varying delays and Markovian jump parameters. First, the Markov chain with partially unknown probabilities is used in this paper to describe the switching between system modes, which can make the model more generalizable. Moreover, considering the possible gain variations, we design a non-fragile state estimator that makes the estimation performance non-fragile to gain variations, thus guaranteeing the estimation performance. Sufficient conditions that ensure the asymptotic stability of the estimation error can be derived by using the Lyapunov stabilization theory and several inequality treatments. Finally, a simulation example is presented to demonstrate the effectiveness of the proposed estimator design scheme.