H∞ state estimation for discrete-time delayed neural networks with randomly occurring quantizations and missing measurements

This paper is concerned with the H∞ state estimation problem for a class of discrete-time neural networks with time-varying delays, randomly occurring quantizations (ROQs) as well as missing measurements. The phenomena of ROQ and missing measurements are governed by a Bernoulli distributed stochastic sequence. The purpose of the addressed problem is to design a state estimator such that the dynamics of the estimation error is exponentially stable in the mean square and the prescribed H∞ performance constraint is satisfied. By constructing proper Lyapunov-Krasovskii functionals and employing stochastic analysis techniques, sufficient conditions are derived to ensure the existence of the desired estimator. Furthermore, the explicit expression of the gain of the desired estimator is described in terms of the solution to a convex optimization problem that can be easily solved by using the semi-definite programme method. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimator design approach.