Outlier-resistant <i>l</i>₂-<i>l</i>_∞ state estimation for discrete-time memristive neural networks with time-delays

In this paper, the outlier-resistant l2-l∞ state estimation issue is investigated for a class of discrete-time memristive neural networks (DMNNs) with time-delays. Measurement outputs could occur unpredictable abnormal data due possibly to outliers from interferences, cyber-attacks as well vibration equipment. Obviously, performance be degraded if these measurements were directly taken into innovation drive dynamics. As such, novel estimator DMNNs time-delays developed diminish adverse effects predictable data. By resorting robust analysis theory and Lyapunov stability theory, some sufficient conditions are established ensure prescribed index while achieving stochastic error Furthermore, desired gains derived by solving convex optimization problem. Finally, simulation example provided demonstrate feasibility proposed design algorithm estimators.