Outlier-resistant l2-l∞ state estimation for discrete-time memristive neural networks with time-delays

• Main Authors: Peifeng Zhao, Hongjian Liu, Guang He, Derui Ding
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2021-01-01
• Series: Systems Science & Control Engineering
• Subjects: memristive neural networks; outlier-resistant state estimation; $ l_2-l_\infty $ performance; time-delays
• Online Access: http://dx.doi.org/10.1080/21642583.2020.1867663

In this paper, the outlier-resistant $ l_2 $ - $ l_\infty $ 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 abnormal interferences, cyber-attacks as well as vibration of equipment. Obviously, the estimation performance could be degraded if these abnormal measurements were directly taken into the innovation to drive the estimation dynamics. As such, a novel outlier-resistant estimator for DMNNs with time-delays is developed to diminish the adverse effects from predictable abnormal data. By resorting to the robust analysis theory and the Lyapunov stability theory, some sufficient conditions are established to ensure a prescribed $ l_2 $ - $ l_\infty $ performance index while achieving the stochastic stability of the estimation error dynamics. Furthermore, the desired estimator gains are derived by solving a convex optimization problem. Finally, a simulation example is provided to demonstrate the feasibility of the proposed design algorithm of outlier-resistant state estimators.