\(H_{\infty }\) State Estimation for Stochastic Jumping Neural Networks with Fading Channels Over a Finite-Time Interval

摘要

In the work, a class of Markov jump neural networks in the discrete-time domain with fading channels are taken into account. The main aim is to investigate the \(H_{\infty }\) state estimation issue when the Rice fading occurs in measured networks. In the first place, the analyses of the finite-time boundedness and the \(H_{\infty }\) performance for the estimation error system with the aid of finite-time stability theory are presented. Some conditions which guarantee the solvability of the addressed problem are established. Furthermore, by applying an unique decoupling method, the gains of the presented estimator are obtained under the feasible solutions of the conditions derived before. Finally, the validity of the presented approach is verified by a numerical example.