Observer-Based Event-Triggered Fault-Tolerant Synchronization for Memristive Neural Networks Subject to Multiple Failures.

IF 8.9 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

IEEE transactions on neural networks and learning systems Pub Date : 2025-08-13 DOI:10.1109/TNNLS.2025.3596704

Mingxin Wang, Song Zhu, Xiaoyang Liu, Shiping Wen, Chaoxu Mu

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Abstract

In this article, the synchronization problem of memristive neural networks (MNNs) subjected to multiple failures is investigated. First, a general form of fault model is introduced into the MNNs, which can represent and summarize various process faults, actuator faults, and their coupling. Subsequently, with the help of designing intermediate variables, two types of fault function observers based on state feedback and output feedback are constructed, and their effectiveness is verified through a generalization of Halanay-type inequalities. Then, based on the designed observers and the event-triggered strategy, two classes of fault-tolerant synchronization schemes are designed for the considered MNNs. By adjusting the controller parameter conditions, finite-time and fixed-time synchronization or quasi-synchronization of the considered MNNs system can be achieved, respectively. Finally, the effectiveness of the provided fault observers and synchronization strategies is verified through simulation and comparison experiments.

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多故障记忆神经网络中基于观测器的事件触发容错同步。

研究了多故障情况下记忆神经网络的同步问题。首先，将故障模型的一般形式引入到MNNs中，该模型可以表示和总结各种过程故障、执行器故障及其耦合。随后，通过设计中间变量，构造了基于状态反馈和输出反馈的两类故障函数观测器，并通过推广halanay型不等式验证了它们的有效性。然后，基于所设计的观测器和事件触发策略，针对所考虑的mnn设计了两类容错同步方案。通过调整控制器参数条件，可以分别实现所考虑的MNNs系统的有限时间和固定时间同步或准同步。最后，通过仿真和对比实验验证了所提供的故障观测器和同步策略的有效性。

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

IEEE transactions on neural networks and learning systems COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, HARDWARE & ARCHITECTURE

CiteScore

23.80

自引率

9.60%

发文量

2102

审稿时长

3-8 weeks

期刊介绍： The focus of IEEE Transactions on Neural Networks and Learning Systems is to present scholarly articles discussing the theory, design, and applications of neural networks as well as other learning systems. The journal primarily highlights technical and scientific research in this domain.