具有随机数据丢失的延迟布尔网络的渐近稳定性。

IF 10.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE IEEE transactions on neural networks and learning systems Pub Date : 2023-08-15 DOI:10.1109/TNNLS.2023.3301220
Chi Huang, Wenjun Xiong, Jianquan Lu, Darong Huang
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引用次数: 0

摘要

在实际网络中,由于通信限制,节点之间往往无法充分交换信息,这是不可避免的。本论文研究了布尔网络(BN)中的时间延迟和随机数据丢失问题。为每个节点分配一个伯努利随机变量来描述数据包丢失的概率。时间延迟和数据丢失由独立随机变量建模。我们提出了一种新颖的数据发送规则,它同时包含了这两种通信约束条件。为进行理论分析,建立了一个由当前状态、延迟信息和成功传输的数据组成的增强系统。利用半张量乘积(STP),得出了具有随机数据丢失的延迟 BN 渐近稳定性的必要条件和充分条件。同时还得到了收敛速率。
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Asymptotic Stability of Delayed Boolean Networks With Random Data Dropouts.

In real networks, communication constraints often prevent the full exchange of information between nodes, which is inevitable. This brief investigates the problem of time delay and randomly missing data in Boolean networks (BNs). A Bernoulli random variable is assigned to each node to characterize the probability of data packet dropout. Time delay and missing data are modeled by independent random variables. A novel data-sending rule that incorporates both communication constraints is proposed. An augmented system, comprising current states, delayed information, and successfully transmitted data, is established for theoretical analysis. Using the semitensor product (STP), the necessary and sufficient condition for asymptotic stability of delayed BNs with random data dropouts is derived. The convergence rate is also obtained.

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来源期刊
IEEE transactions on neural networks and learning systems
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.
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