控制理论与应用2025，Vol.42Issue(10)：1990-1998,9.DOI:10.7641/CTA.2024.30656
基于非降阶法的模糊惯性神经网络的固定时间同步
Fixed-time synchronization of fuzzy inertial neural networks based on non-reduced order approach
摘要
Abstract
Up to now,the fixed-time synchronization analysis of inertial neural networks has mainly utilized variable substitution method,which is effective,but expands the system's dimension and eliminates the influence of the inertial term.In order to consider the intuitive effects of the inertia term,this paper discusses the fixed-time synchronization of delayed fuzzy neural networks with inertia terms via using the non-reduced-order approach.Under the Filippov solution framework and finite time stability theory,some synchronization criteria are obtained to ensure the realization of fixed-time synchronization of the proposed neural system by designing nonlinear feedback controller.In addition,the upper bound of the synchronization time is estimated by using some inequality techniques,which can provide reliability guarantee for its application in practical engineering.Finally,the results obtained in this article are verified through numerical examples and an image encryption application.关键词
不连续激活函数/模糊惯性神经网络/固定时间同步/非降阶法Key words
discontinuous activation/fuzzy inertial neural networks/fixed-time synchronization/non-reduced order approach引用本文复制引用
陈腾,戴厚平,龙常青..基于非降阶法的模糊惯性神经网络的固定时间同步[J].控制理论与应用,2025,42(10):1990-1998,9.基金项目
湖南省自然科学基金项目(2021JJ30548),湖南省教育厅科学研究重点项目(21A0329)资助.Supported by the Natural Science Foundation of Hunan Province(2021JJ30548)and the Key Projects of Hunan Provincial Department of Education(21A0329). （2021JJ30548）
