具有时延的离散时变阶分数神经网络的完全同步化

IF 4.6 2区物理与天体物理 Q1 PHYSICS, MULTIDISCIPLINARY

Chinese Journal of Physics Pub Date : 2024-08-20 DOI:10.1016/j.cjph.2024.08.022

Tong Li , Hong-Li Li , Long Zhang , Song Zheng

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引用次数: 0

摘要

本文研究了具有时间延迟的离散时间变阶分数神经网络（DVFNN）的完全同步问题。通过离散不等式技术和纳布拉-拉普拉斯变换，推导出两个稳定性定理，它们是对恒定阶情况的概括。此外，还利用不等式技术和 Lyapunov 方法提出了几种 DVFNN 的完全同步准则。最后，本文提供了一个数值示例来验证理论结果。本文还提供了变阶分数离散时间系统的稳定性分析方法。

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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Complete synchronization of discrete-time variable-order fractional neural networks with time delays

This paper investigates complete synchronization of discrete-time variable-order fractional neural networks (DVFNNs) with time delays. By discrete inequality technologies and nabla Laplace transform, two stability lemmas are derived which are generalizations of the constant-order case. Furthermore, several complete synchronization criteria for DVFNNs are proposed by utilizing inequality techniques and Lyapunov method. Finally, a numerical example is provided to verify the theoretical results. This paper also provides a stability analysis method for variable-order fractional discrete-time systems.

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来源期刊

Chinese Journal of Physics 物理-物理：综合

CiteScore

8.50

自引率

10.00%

发文量

361

审稿时长

44 days

期刊介绍： The Chinese Journal of Physics publishes important advances in various branches in physics, including statistical and biophysical physics, condensed matter physics, atomic/molecular physics, optics, particle physics and nuclear physics. The editors welcome manuscripts on: -General Physics: Statistical and Quantum Mechanics, etc.- Gravitation and Astrophysics- Elementary Particles and Fields- Nuclear Physics- Atomic, Molecular, and Optical Physics- Quantum Information and Quantum Computation- Fluid Dynamics, Nonlinear Dynamics, Chaos, and Complex Networks- Plasma and Beam Physics- Condensed Matter: Structure, etc.- Condensed Matter: Electronic Properties, etc.- Polymer, Soft Matter, Biological, and Interdisciplinary Physics. CJP publishes regular research papers, feature articles and review papers.