具有边界反馈的二维细胞神经网络的指数同步

IF 0.8 4区数学 Q3 MATHEMATICS, APPLIED

Stochastic Analysis and Applications Pub Date : 2023-11-08 DOI:10.1080/07362994.2023.2260871

Leslaw Skrzypek, Chi Phan, Yuncheng You

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引用次数: 0

摘要

本文提出了一种基于边界反馈的格子FitzHugh-Nagumo方程的二维细胞神经网络(CNN)新模型，并通过解的\emph{先验}一致估计和耗散动力学分析证明了整个神经网络指数同步的一个阈值条件。网络成对边界单元间间隙信号所满足的阈值用结构参数表示，可调。本文的新结果和方法也可以推广到三维及高维的FitzHugh-Nagumo型或Hindmarsh-Rose型细胞神经网络。

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Exponential synchronization of 2D cellular neural networks with boundary feedback

In this work we propose a new model of 2D cellular neural networks (CNN) in terms of the lattice FitzHugh-Nagumo equations with boundary feedback and prove a threshold condition for the exponential synchronization of the entire neural network through the \emph{a priori} uniform estimates of solutions and the analysis of dissipative dynamics. The threshold to be satisfied by the gap signals between pairwise boundary cells of the network is expressed by the structural parameters and adjustable. The new result and method of this paper can also be generalized to 3D and higher dimensional FitzHugh-Nagumo type or Hindmarsh-Rose type cellular neural networks.

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来源期刊

Stochastic Analysis and Applications 数学-统计学与概率论

CiteScore

2.70

自引率

7.70%

发文量

审稿时长

6-12 weeks

期刊介绍： Stochastic Analysis and Applications presents the latest innovations in the field of stochastic theory and its practical applications, as well as the full range of related approaches to analyzing systems under random excitation. In addition, it is the only publication that offers the broad, detailed coverage necessary for the interfield and intrafield fertilization of new concepts and ideas, providing the scientific community with a unique and highly useful service.