Finite-time dissipative synchronization for state-dependent switching delayed neural networks via sampled-data control

IF 9 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Engineering Applications of Artificial Intelligence Pub Date : 2025-12-24 Epub Date: 2025-10-13 DOI:10.1016/j.engappai.2025.112613

Jinrong Yang , Guici Chen , Shiping Wen , Yin Sheng

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引用次数: 0

Abstract

This paper investigates the issue of finite-time dissipative synchronization for delayed neural networks through the design of a sampled-data controller. The system is characterized by discontinuous parameters and exhibits state-dependent switching behaviors. To handle the challenges posed by these switching characteristics, the interval matrix method is utilized to transform the switching system into one with interval parameters. Then several sufficient conditions for finite-time boundedness of the error system are derived by constructing an appropriate Lyapunov–Krasovskii functional. Building upon these conditions, criteria for finite-time

(Q_{1}, Q_{2}, Q_{3}) - ψ

dissipative synchronization are further established using advanced integral inequality techniques. Moreover, a sampled-data controller incorporating state-dependent switching parameters is designed by solving the linear matrix inequality. A circuit model of the switched delayed neural networks is presented to illustrate the engineering significance and feasibility of the theoretical results. Finally, numerical simulations are provided to demonstrate the effectiveness and advantages of the proposed control strategy.

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基于采样数据控制的状态依赖切换延迟神经网络有限时间耗散同步

本文通过设计一种采样数据控制器来研究延迟神经网络的有限时间耗散同步问题。该系统具有不连续参数和状态依赖切换特性。为了解决这些切换特性带来的挑战，利用区间矩阵法将切换系统转化为具有区间参数的切换系统。然后通过构造合适的Lyapunov-Krasovskii泛函，得到了误差系统有限时间有界的几个充分条件。在这些条件的基础上，利用先进的积分不等式技术进一步建立了有限时间(Q1,Q2,Q3)−ψ耗散同步的判据。此外，通过求解线性矩阵不等式，设计了包含状态相关开关参数的采样数据控制器。最后给出了切换延迟神经网络的电路模型，以说明理论结果的工程意义和可行性。最后，通过数值仿真验证了所提控制策略的有效性和优越性。

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

Engineering Applications of Artificial Intelligence 工程技术-工程：电子与电气

CiteScore

9.60

自引率

10.00%

发文量

505

审稿时长

68 days

期刊介绍： Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.