具有泄漏和模相关延迟的马尔可夫跳变中立型神经网络的无源性分析

IF 1.1 Q4 BIOPHYSICS

AIMS Biophysics Pub Date : 2023-01-01 DOI:10.3934/biophy.2023012

N. Mala, Arumugam Vinodkumar, J. Alzabut

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引用次数: 1

摘要

本文讨论了神经型马尔可夫跳变神经网络的无源性分析。利用线性矩阵不等式的相位以及三重积分项和四重积分项的改进Lyapunov-Krasovskii泛函(LKF)证明了结果。在构造的Lyapunov-Krasovskii泛函中考虑了所有时滞的模态相关信息，导出了新的稳定性判据。选择尽可能多的李雅普诺夫矩阵的价值是模相关的证明。最后通过数值算例证明了上述理论结果的有效性和降低了保守性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Passivity analysis for Markovian jumping neutral type neural networks with leakage and mode-dependent delay

In this study, we discuss the passivity analysis for Markovian jumping Neural Networks of neural-type. The results are demonstrated using phases of linear matrix inequalities as well as an improved Lyapunov-Krasovskii functional (LKF) of the triple integral terms and quadruple integrals. The information of the mode-dependent of all delays have been taken into account in the constructed Lyapunov–Krasovskii functional and novel stability criterion is derived. The value of selecting as many Lyapunov matrices that are mode-dependent as possible is demonstrated. The effectiveness and decreased conservatism of the aforementioned theoretical results are eventually demonstrated by a numerical example.

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来源期刊

AIMS Biophysics BIOPHYSICS-

CiteScore

2.40

自引率

20.00%

发文量

审稿时长

8 weeks

期刊介绍： AIMS Biophysics is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of biophysics. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Biophysics welcomes, but not limited to, the papers from the following topics: · Structural biology · Biophysical technology · Bioenergetics · Membrane biophysics · Cellular Biophysics · Electrophysiology · Neuro-Biophysics · Biomechanics · Systems biology