Finite-time state estimation for delayed periodic neural networks over multiple-packet transmission

In this paper, the problem of finite-time state estimation for delayed periodic neural networks over multiple-packet transmission is addressed. The components of measurement output are separately transmitted by multiple-packet transmission, and the randomly occurring packet dropouts of different channels are described by mutually independent Bernoulli processes. In order to improve the robustness of the estimator, a non-fragile estimator is designed. In addition, some sufficient criteria are given to ensure that the estimation error system is stochastically finite-time stable and stochastically finite-time bounded, and the gains of non-fragile estimator are then derived based on these results. Finally, simulation results are provided to illustrate the effectiveness of the proposed estimator design approach.