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具有非必然可微时变延迟的忆阻神经网络的\( \mathcal{L}_{2}-\mathcal{L}_{\infty} \)控制

$ \mathcal{L}_{2}-\mathcal{L}_{\infty} $ control for memristive NNs with non-necessarily differentiable time-varying delay.

作者信息

Wang Jingya, Zhu Ye

机构信息

School of Computer Science and Technology, Anhui University of Technology, Ma'anshan 243032, China.

出版信息

Math Biosci Eng. 2023 Jun 8;20(7):13182-13199. doi: 10.3934/mbe.2023588.

Abstract

This paper investigates $ \mathcal{L}{2}-\mathcal{L}{\infty} $ control for memristive neural networks (MNNs) with a non-necessarily differentiable time-varying delay. The objective is to design an output-feedback controller to ensure the $ \mathcal{L}{2}-\mathcal{L}{\infty} $ stability of the considered MNN. A criterion on the $ \mathcal{L}{2}-\mathcal{L}{\infty} $ stability is proposed using a Lyapunov functional, the Bessel-Legendre inequality, and the convex combination inequality. Then, a linear matrix inequalities-based design scheme for the required output-feedback controller is developed by decoupling nonlinear terms. Finally, two examples are presented to verify the proposed $ \mathcal{L}{2}-\mathcal{L}{\infty} $ stability criterion and design method.

摘要

本文研究具有非必然可微时变延迟的忆阻神经网络(MNN)的(\mathcal{L}{2}-\mathcal{L}{\infty})控制。目标是设计一个输出反馈控制器,以确保所考虑的MNN的(\mathcal{L}{2}-\mathcal{L}{\infty})稳定性。利用李雅普诺夫泛函、贝塞尔 - 勒让德不等式和凸组合不等式,提出了一个关于(\mathcal{L}{2}-\mathcal{L}{\infty})稳定性的准则。然后,通过对非线性项进行解耦,开发了一种基于线性矩阵不等式的所需输出反馈控制器设计方案。最后,给出两个例子来验证所提出的(\mathcal{L}{2}-\mathcal{L}{\infty})稳定性准则和设计方法。

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