带符号图的耦合四元值神经网络的两方安全同步标准

IF 6 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Neural Networks Pub Date : 2024-09-11 DOI:10.1016/j.neunet.2024.106717
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引用次数: 0

摘要

本研究探讨了耦合四元值神经网络(QVNN)的双向安全同步问题,其中考虑了变量采样通信和随机欺骗攻击。首先,利用符号图理论,建立了具有结构平衡的合作-竞争交互的耦合 QVNN 的数学模型。其次,通过采用非分解方法和构造合适的单元 Lyapunov 函数,以四元数值 LMI 的形式得到了耦合 QVNN 的双向安全同步(BSS)准则。值得一提的是,结构平衡拓扑相对较强,因此对结构不平衡图的耦合 QVNNs 进行了进一步研究。将结构不平衡图视为结构平衡图的中断,推导出具有结构不平衡图的耦合 QVNN 的双向安全准同步(BSQS)准则。最后,给出了两个模拟,以说明所建议的 BSS 和 BSQS 方法的可行性。
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Bipartite secure synchronization criteria for coupled quaternion-valued neural networks with signed graph

This study explores the bipartite secure synchronization problem of coupled quaternion-valued neural networks (QVNNs), in which variable sampled communications and random deception attacks are considered. Firstly, by employing the signed graph theory, the mathematical model of coupled QVNNs with structurally-balanced cooperative–competitive interactions is established. Secondly, by adopting non-decomposition method and constructing a suitable unitary Lyapunov functional, the bipartite secure synchronization (BSS) criteria for coupled QVNNs are obtained in the form of quaternion-valued LMIs. It is essential to mention that the structurally-balanced topology is relatively strong, hence, the coupled QVNNs with structurally-unbalanced graph are further studied. The structurally-unbalanced graph is treated as an interruption of the structurally-balanced graph, the bipartite secure quasi-synchronization (BSQS) criteria for coupled QVNNs with structurally-unbalanced graph are derived. Finally, two simulations are given to illustrate the feasibility of the suggested BSS and BSQS approaches.

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来源期刊
Neural Networks
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.
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