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Updated: Mar 15, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
H∞ state estimation for memristive neural networks with time-varying delays: The discrete-time case
Sanbo Ding1, Zhanshan Wang1, Jidong Wang1
1School of Information Science and Engineering, Northeastern University, Shenyang 110819, China.
Abstract:
This paper investigates the H∞ state estimation problem for a class of discrete-time memristive neural networks (DMNNs) with time-varying delays. For the sake of coping with the switched weight matrices, the DMNNs are recast into a tractable model by defining a series of state-dependent switched signals. Based on the tractable model, the robust analysis method and Lyapunov stability theory are developed to devise a sufficient condition which ensures the global asymptotical stability of the estimation error system with a prescribed H∞ performance. The desired state estimator gain matrix and optimal performance index can be accomplished via solving a convex optimization problem subject to several linear matrix inequalities (LMIs). Finally, one numerical example is presented to check the effectiveness of the theoretical results.
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