Network-based H_∞ state estimation for neural networks using imperfect measurement

This study considers the network-based H_∞ state estimation problem for neural networks where transmitted measurements suffer from the sampling effect, external disturbance, network-induced delay, and packet dropout as network constraints. The external disturbance, network-induced delay, and packet dropout affect the measurements at only the sampling instants owing to the sampling effect. In addition, when packet dropout occurs, the last received data are used. To tackle the imperfect signals, a compensator is designed, and then by aid of the compensator, H_∞ filter which guarantees desired performance is designed as well. A numerical example is given to illustrate the validity of the proposed methods.