Non-fragile state estimation for memristive cellular neural networks with proportional delay

Abstract

This paper focuses on modeling a non-fragile state estimator for a class of memristive cellular neural networks (MCNNs) with proportional delay. Due to the state transition characteristics of memristor, the parameters of MCNNs are state-dependent. A discontinuous robust control scheme is applied to address such parameters issue. Using this control scheme, we have derived sufficient conditions to ensure the existence of a non-fragile state estimator for the supposed system. Through the Lyapunov stability analysis and matrix-based inequality techniques, delay-dependent stability criteria are obtained in the form of linear matrix inequalities (LMIs), which shows the asymptotic stableness of the prescribed error system under the consideration of all possible gain variations. Besides, the control gain components are obtained by solving the resulting LMIs using some available MATLAB algorithms. Lastly, to facilitate the efficacy of the proposed estimator design, numerical simulations are examined.