Finite-time H<inf>∞</inf> bounded estimation for memristive recurrent neural networks with randomly occurring time-delay and missing measurements

Abstract

In this paper, the finite-time H ∞ state estimation problem is investigated for a class of discrete-time memristive recurrent neural networks (DMRNNs) subject to randomly occurring time-delay and missing measurements. Two random variables obeying the Bernoulli distribution are employed to characterize the randomly occurring time-delay and missing measurements, where the corresponding occurrence probabilities are assumed to be known. The main purpose of this paper is to design an H ∞ state estimator such that the estimation error dynamics is finite-time bounded with respect to some prescribed parameters and the H ∞ performance is achieved simultaneously. In view of the semi-definite programming approach, sufficient conditions are given to guarantee the existence of desired state estimator and provide the explicit form of the estimator gain. Finally, a numerical simulation example is given to verify the feasibility and effectiveness of the developed estimation approach.