Fixed/prescribed-time synchronization of state-dependent switching neural networks with stochastic disturbance and impulsive effects

IF 6.3 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2025-09-11 DOI:10.1016/j.neunet.2025.108100

Guici Chen , Houxuan Zhang , Shiping Wen , Junhao Hu , Leimin Wang

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引用次数: 0

Abstract

This paper investigates the fixed-time synchronization (FXTS) and prescribed-time synchronization (PSTS) problems of state-dependent switching neural networks (SDSNNs) with stochastic disturbances and impulsive effects. By leveraging the average impulsive interval, comparison principle, and interval matrix methodology, this study advances a novel analytical framework. Departing from conventional approaches, we reformulate stochastic disturbed and impulsive SDSNNs as interval-parameter systems through rigorous interval matrix transformation. Consequently, we derive some sufficient conditions in the form of linear matrix inequalities (LMIs) to ensure the realization of FXTS and PSTS. Since impulsive effects can potentially compromise synchronization stability, careful controller design becomes critical. To address this challenge, we develop a unified proportional integral (PI) control framework. Through proper adjustment of its control parameters, this framework enables the system to achieve both FXTS and PSTS. Moreover, by reasonably configuring the relationship between the impulsive intensity and the prescribed time, the synchronization performance can be balanced. Finally, we demonstrate the effectiveness of the theoretical results through two examples.

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具有随机干扰和脉冲效应的状态依赖切换神经网络的固定/规定时间同步

研究了具有随机干扰和脉冲效应的状态相关切换神经网络的固定时间同步（FXTS）和规定时间同步（PSTS）问题。通过利用平均脉冲区间、比较原理和区间矩阵方法，本研究提出了一个新的分析框架。与传统方法不同，我们通过严格的区间矩阵变换，将随机扰动和脉冲sdsnn重新表述为区间参数系统。因此，我们以线性矩阵不等式（lmi）的形式导出了保证FXTS和PSTS实现的一些充分条件。由于脉冲效应可能潜在地损害同步稳定性，因此谨慎的控制器设计变得至关重要。为了应对这一挑战，我们开发了一个统一的比例积分（PI）控制框架。通过适当调整其控制参数，该框架使系统能够同时实现FXTS和PSTS。此外，通过合理配置脉冲强度与规定时间的关系，可以平衡同步性能。最后，通过两个算例验证了理论结果的有效性。

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

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.