H_∞ state estimation for memristive neural networks with time-varying delays: The discrete-time case.

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.