Finite-Horizon $H_{\infty}$ State Estimation for Periodic Neural Networks Over Fading Channels
Xiaomeng Li, Bin Zhang, Panshuo Li, Qi Fan Zhou, Renquan Lu · IEEE Transactions on Neural Networks and Learning Systems · 2019
The problem of finite-horizon H∞state estimator design for periodic neural networks over multiple fading channels is studied in this paper. To characterize the measurement signals transmitted through different channels experiencing channel fading, a multiple fading channels model is considered. For investigating the situation of correlated fading channels, a set of correlated random variables is introduced. Specifically, the channel coefficients are described by white noise processes and are assumed to be correlated. Two sufficient criteria are provided, by utilizing a stochastic analysis approach, to guarantee that the estimation error system is stochastically stable and achieves the prescribed H∞performance. Then, the parameters of the estimator are derived by solving recursive linear matrix inequalities. Finally, some simulation results are shown to illustrate the effectiveness of the proposed method.