.png){kind=icon}
返回
Net Learning
.png){kind=icon}
PRE
AI
DOI：10.1109/TNNLS.2021.3084902.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Graph neural networks, which generalize deep learning to graph-structured data, have achieved significant improvements in numerous graph-related tasks. Petri nets (PNs), on the other hand, are mainly used for the modeling and analysis of various event-driven systems from the perspective of prior knowledge, mechanisms, and tasks. Compared with graph data, net data can simulate the dynamic behavioral features of systems and are more suitable for representing real-world problems. However, the problem of large-scale data analysis has been puzzling the PN field for decades, and thus, limited its universal applicability. In this article, a framework of net learning (NL) is proposed. NL contains the advantages of PN modeling and analysis with the advantages of graph learning computation. Then, two kinds of NL algorithms are designed for performance analysis of stochastic PNs, and more specifically, the hidden feature information of the PN is obtained by mapping net information to the low-dimensional feature space. Experiments demonstrate the effectiveness of the proposed model and algorithms on the performance analysis of stochastic PNs.
Keyword:
Analytical models
Petri nets
Performance analysis
Task analysis
Random variables
Mathematical model
Markov processes
Graph neural network (GNN)
net learning (NL)
Petri net (PN)
stochastic PN (SPN)
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
8.9
论文数:
7.6K
被引数:
7.2W
机构
引用论文
Coordination of Ln3+ in ortho-tetramethyl-substituted cucurbituril supramolecular assemblies formed in the presence of cadmium nitrate: potential applications for isolation of heavier lanthanides.png)
CrystEngComm
CrystEngCommIF0
PREAI
分享收藏
Passive signal propagation and membrane properties in median photoreceptors of the giant barnacle
OAAI
OAAI分享收藏A Survey on Acquisition, Tracking, and Pointing Mechanisms for Mobile Free-Space Optical Communications移动自由空间光通信的捕获，跟踪和指向机制综述
PREAI
分享收藏
查看更多内容