Synchronization of nonlinear neural networks with hybrid couplings and uncertain time-varying perturbations: A novel distributed-delay impulsive comparison principle

IF 5.5 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Hongguang Fan , Kaibo Shi , Yi Zhao
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引用次数: 0

Abstract

This paper investigates the synchronization of nonlinear drive-response neural networks subject to uncertain time-varying perturbations, non-delayed coupling, and distributed delay coupling. To address the influence of distributed and discrete delays on the system, we establish a novel impulsive comparison principle, extending the Halanay inequality. By leveraging Lyapunov stability theory, we derive sufficient conditions for the exponential synchronization of the neural networks using a delayed impulsive controller with historical status information. This approach relaxes the conventional constraint that impulsive delays must be smaller than impulsive intervals, thereby generalizing existing synchronization results for distributed delay networks. Numerical simulations for chaotic neural networks validate the theoretical results and demonstrate the sensitivity of the control gain matrix.
具有混合耦合和不确定时变扰动的非线性神经网络的同步:新型分布式延迟脉冲比较原理
本文研究了受不确定时变扰动、非延迟耦合和分布延迟耦合影响的非线性驱动-响应神经网络的同步问题。为了解决分布式延迟和离散延迟对系统的影响,我们扩展了哈拉内不等式,建立了一个新的脉冲比较原理。通过利用 Lyapunov 稳定性理论,我们推导出了使用具有历史状态信息的延迟脉冲控制器实现神经网络指数同步的充分条件。这种方法放宽了脉冲延迟必须小于脉冲间隔的传统约束,从而推广了分布式延迟网络的现有同步结果。混沌神经网络的数值模拟验证了理论结果,并证明了控制增益矩阵的敏感性。
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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来源期刊
Neurocomputing
Neurocomputing 工程技术-计算机:人工智能
CiteScore
13.10
自引率
10.00%
发文量
1382
审稿时长
70 days
期刊介绍: Neurocomputing publishes articles describing recent fundamental contributions in the field of neurocomputing. Neurocomputing theory, practice and applications are the essential topics being covered.
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