Reliable state estimation for neural networks with TOD protocol and mixed compensation.

This paper considers the reliable state estimation issue for discrete-time neural networks with the try-once-discard (TOD) scheduling protocol and mixed compensation strategy. For the phenomenon of medium access constraint, the measurement transmitted from sensors to the estimator is subjected to the TOD scheduling protocol. The mixed compensation is proposed to flexibly compensate those missing measurements caused by the TOD protocol. By using a novel polytopic uncertain model, a reliable state estimator is designed, where the gain matrix is determined by two vertex matrices. Then sufficient conditions are established, which ensure the error system meets the stochastic stability and the l 2 - l ∞ performance. Finally, an illustrative example shows the validity of the proposed reliable state estimator. [ABSTRACT FROM AUTHOR]