H∞H∞ state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances

This paper considers the problem of delay-dependent H∞H∞ state estimation for discrete-time neural networks with interval time-varying delays and probabilistic diverging disturbances. By constructing a newly augmented Lyapunov–Krasovskii functional, a less conservative criterion for the existence of the estimator of discrete-time neural networks without disturbances is introduced in Theorem 1 with the framework of linear matrix inequalities (LMIs). Based on the result of Theorem 1, a designing criterion of the estimator for a newly constructed error dynamic system with probabilistic diverging disturbances between original system and estimator will be proposed in Theorem 2. Two numerical examples are given to show the improvements over the existing ones and the effectiveness of the proposed idea.