This paper investigates the state estimation problem for a class of fractional-order memristive neural networks (FOMNNs) with leakage and time delay. The main objective of this study is to construct an efficient estimator such that the state of the corresponding estimation error is globally stable. Distinct to the previous studies, the state estimation problem of FOMNNs is investigated through fractional-order Lyapunov direct method. The sufficient conditions that ensure the global stability of the error system has been derived as a set of solvable linear matrix inequalities. In order to validate the effectiveness of the proposed theoretical results, two numerical examples have been illustrated.

本文研究了一类具有泄漏和时滞的分数阶忆阻神经网络（FOMNN）的状态估计问题。这项研究的主要目的是构造一个有效的估计器，以使相应估计误差的状态全局稳定。与以前的研究不同，通过分数阶Lyapunov直接方法研究了FOMNN的状态估计问题。作为一组可解线性矩阵不等式，得出了确保误差系统全局稳定性的充分条件。为了验证所提出的理论结果的有效性，已说明了两个数值示例。